id	sid	tid	token	lemma	pos
cana-1285	1	1	communications	communication	NOUN
cana-1285	1	2	on	on	ADP
cana-1285	1	3	applied	apply	VERB
cana-1285	1	4	nonlinear	nonlinear	ADJ
cana-1285	1	5	analysis	analysis	NOUN
cana-1285	1	6	issn	issn	NOUN
cana-1285	1	7	:	:	PUNCT
cana-1285	1	8	1074	1074	NUM
cana-1285	1	9	-	-	PUNCT
cana-1285	1	10	133x	133x	NUM
cana-1285	1	11	vol	vol	NOUN
cana-1285	1	12	31	31	NUM
cana-1285	1	13	no	no	NOUN
cana-1285	1	14	.	.	PUNCT
cana-1285	2	1	7s	7	NOUN
cana-1285	2	2	(	(	PUNCT
cana-1285	2	3	2024	2024	NUM
cana-1285	2	4	)	)	PUNCT
cana-1285	2	5	67	67	NUM
cana-1285	2	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1285	2	7	integrating	integrate	VERB
cana-1285	2	8	nonlinear	nonlinear	ADJ
cana-1285	2	9	dynamics	dynamic	NOUN
cana-1285	2	10	and	and	CCONJ
cana-1285	2	11	statistical	statistical	ADJ
cana-1285	2	12	methods	method	NOUN
cana-1285	2	13	in	in	ADP
cana-1285	2	14	deep	deep	ADJ
cana-1285	2	15	learning	learning	NOUN
cana-1285	2	16	models	model	NOUN
cana-1285	2	17	for	for	ADP
cana-1285	2	18	biochemical	biochemical	ADJ
cana-1285	2	19	component	component	NOUN
cana-1285	2	20	analysis	analysis	NOUN
cana-1285	2	21	1swamydoss	1swamydoss	NUM
cana-1285	2	22	d	d	PROPN
cana-1285	2	23	,	,	PUNCT
cana-1285	2	24	adhiyamaan	adhiyamaan	PROPN
cana-1285	2	25	college	college	PROPN
cana-1285	2	26	of	of	ADP
cana-1285	2	27	engineering	engineering	PROPN
cana-1285	2	28	,	,	PUNCT
cana-1285	2	29	hosur	hosur	PROPN
cana-1285	2	30	,	,	PUNCT
cana-1285	2	31	india	india	PROPN
cana-1285	2	32	.	.	PUNCT
cana-1285	3	1	swamyasir@gmail.com	swamyasir@gmail.com	X
cana-1285	3	2	2o.a.sridevi	2o.a.sridevi	NUM
cana-1285	3	3	,	,	PUNCT
cana-1285	3	4	assistant	assistant	NOUN
cana-1285	3	5	professor	professor	NOUN
cana-1285	3	6	,	,	PUNCT
cana-1285	3	7	department	department	NOUN
cana-1285	3	8	of	of	ADP
cana-1285	3	9	science	science	NOUN
cana-1285	3	10	and	and	CCONJ
cana-1285	3	11	humanities	humanity	NOUN
cana-1285	3	12	,	,	PUNCT
cana-1285	3	13	karpagam	karpagam	PROPN
cana-1285	3	14	institute	institute	PROPN
cana-1285	3	15	of	of	ADP
cana-1285	3	16	technology	technology	PROPN
cana-1285	3	17	,	,	PUNCT
cana-1285	3	18	coimbatore	coimbatore	PROPN
cana-1285	3	19	,	,	PUNCT
cana-1285	3	20	tamilnadu	tamilnadu	NOUN
cana-1285	3	21	,	,	PUNCT
cana-1285	3	22	india	india	PROPN
cana-1285	3	23	.	.	PUNCT
cana-1285	3	24	sridevi6123@gmail.com	sridevi6123@gmail.com	X
cana-1285	4	1	3sandhya	3sandhya	NUM
cana-1285	4	2	s	s	PROPN
cana-1285	4	3	,	,	PUNCT
cana-1285	4	4	assistant	assistant	NOUN
cana-1285	4	5	professor	professor	NOUN
cana-1285	4	6	,	,	PUNCT
cana-1285	4	7	department	department	PROPN
cana-1285	4	8	of	of	ADP
cana-1285	4	9	cse	cse	PROPN
cana-1285	4	10	,	,	PUNCT
cana-1285	4	11	jct	jct	PROPN
cana-1285	4	12	college	college	PROPN
cana-1285	4	13	of	of	ADP
cana-1285	4	14	engineering	engineering	NOUN
cana-1285	4	15	and	and	CCONJ
cana-1285	4	16	technology	technology	NOUN
cana-1285	4	17	,	,	PUNCT
cana-1285	4	18	coimbatore	coimbatore	PROPN
cana-1285	4	19	,	,	PUNCT
cana-1285	4	20	india	india	PROPN
cana-1285	4	21	.	.	PUNCT
cana-1285	4	22	sandhya9230@gmail.com	sandhya9230@gmail.com	X
cana-1285	5	1	4mulugeta	4mulugeta	NUM
cana-1285	5	2	tesema	tesema	ADJ
cana-1285	5	3	,	,	PUNCT
cana-1285	5	4	associate	associate	NOUN
cana-1285	5	5	professor	professor	NOUN
cana-1285	5	6	,	,	PUNCT
cana-1285	5	7	department	department	NOUN
cana-1285	5	8	of	of	ADP
cana-1285	5	9	chemistry	chemistry	NOUN
cana-1285	5	10	(	(	PUNCT
cana-1285	5	11	analytical	analytical	ADJ
cana-1285	5	12	)	)	PUNCT
cana-1285	5	13	,	,	PUNCT
cana-1285	5	14	college	college	NOUN
cana-1285	5	15	of	of	ADP
cana-1285	5	16	natural	natural	ADJ
cana-1285	5	17	and	and	CCONJ
cana-1285	5	18	computational	computational	ADJ
cana-1285	5	19	sciences	science	NOUN
cana-1285	5	20	,	,	PUNCT
cana-1285	5	21	dambi	dambi	NOUN
cana-1285	5	22	dollo	dollo	PROPN
cana-1285	5	23	university	university	PROPN
cana-1285	5	24	,	,	PUNCT
cana-1285	5	25	dambi	dambi	NOUN
cana-1285	5	26	dollo	dollo	PROPN
cana-1285	5	27	,	,	PUNCT
cana-1285	5	28	ethiopia	ethiopia	PROPN
cana-1285	5	29	.	.	PUNCT
cana-1285	5	30	efa.ntust@gmail.com	efa.ntust@gmail.com	PROPN
cana-1285	5	31	5a.shalini	5a.shalini	NUM
cana-1285	5	32	,	,	PUNCT
cana-1285	5	33	assistant	assistant	NOUN
cana-1285	5	34	professor	professor	NOUN
cana-1285	5	35	,	,	PUNCT
cana-1285	5	36	department	department	PROPN
cana-1285	5	37	of	of	ADP
cana-1285	5	38	cse	cse	PROPN
cana-1285	5	39	,	,	PUNCT
cana-1285	5	40	karpagam	karpagam	PROPN
cana-1285	5	41	academy	academy	PROPN
cana-1285	5	42	of	of	ADP
cana-1285	5	43	higher	high	ADJ
cana-1285	5	44	education	education	NOUN
cana-1285	5	45	,	,	PUNCT
cana-1285	5	46	coimbatore	coimbatore	PROPN
cana-1285	5	47	,	,	PUNCT
cana-1285	5	48	india	india	PROPN
cana-1285	5	49	.	.	PUNCT
cana-1285	6	1	shaliniasokan04@gmail.com	shaliniasokan04@gmail.com	X
cana-1285	6	2	6g.s.bansode	6g.s.bansode	NOUN
cana-1285	6	3	,	,	PUNCT
cana-1285	6	4	assistant	assistant	NOUN
cana-1285	6	5	professor	professor	NOUN
cana-1285	6	6	,	,	PUNCT
cana-1285	6	7	department	department	PROPN
cana-1285	6	8	of	of	ADP
cana-1285	6	9	english	english	PROPN
cana-1285	6	10	,	,	PUNCT
cana-1285	6	11	koneru	koneru	PROPN
cana-1285	6	12	lakshmiah	lakshmiah	PROPN
cana-1285	6	13	education	education	PROPN
cana-1285	6	14	foundation	foundation	PROPN
cana-1285	6	15	,	,	PUNCT
cana-1285	6	16	kl	kl	PROPN
cana-1285	6	17	(	(	PUNCT
cana-1285	6	18	deemed	deem	VERB
cana-1285	6	19	to	to	PART
cana-1285	6	20	be	be	AUX
cana-1285	6	21	)	)	PUNCT
cana-1285	6	22	university	university	NOUN
cana-1285	6	23	,	,	PUNCT
cana-1285	6	24	vijayawada	vijayawada	PROPN
cana-1285	6	25	-	-	PUNCT
cana-1285	6	26	gunturu	gunturu	PROPN
cana-1285	6	27	,	,	PUNCT
cana-1285	6	28	andhra	andhra	PROPN
cana-1285	6	29	pradesh	pradesh	PROPN
cana-1285	6	30	,	,	PUNCT
cana-1285	6	31	india	india	PROPN
cana-1285	6	32	.	.	PUNCT
cana-1285	7	1	bansodegs@kluniversity.in	bansodegs@kluniversity.in	PROPN
cana-1285	7	2	article	article	NOUN
cana-1285	7	3	history	history	NOUN
cana-1285	7	4	:	:	PUNCT
cana-1285	7	5	received	receive	VERB
cana-1285	7	6	:	:	PUNCT
cana-1285	7	7	01	01	NUM
cana-1285	7	8	-	-	PUNCT
cana-1285	7	9	06	06	NUM
cana-1285	7	10	-	-	PUNCT
cana-1285	7	11	2024	2024	NUM
cana-1285	7	12	revised	revise	VERB
cana-1285	7	13	:	:	PUNCT
cana-1285	7	14	03	03	NUM
cana-1285	7	15	-	-	PUNCT
cana-1285	7	16	07	07	NUM
cana-1285	7	17	-	-	PUNCT
cana-1285	7	18	2024	2024	NUM
cana-1285	7	19	accepted	accept	VERB
cana-1285	7	20	:	:	PUNCT
cana-1285	7	21	29	29	NUM
cana-1285	7	22	-	-	SYM
cana-1285	7	23	07	07	NUM
cana-1285	7	24	-	-	PUNCT
cana-1285	7	25	2024	2024	NUM
cana-1285	7	26	abstract	abstract	NOUN
cana-1285	7	27	:	:	PUNCT
cana-1285	7	28	nonlinear	nonlinear	ADJ
cana-1285	7	29	correlations	correlation	NOUN
cana-1285	7	30	in	in	ADP
cana-1285	7	31	data	datum	NOUN
cana-1285	7	32	that	that	SCONJ
cana-1285	7	33	analysis	analysis	NOUN
cana-1285	7	34	of	of	ADP
cana-1285	7	35	biological	biological	ADJ
cana-1285	7	36	components	component	NOUN
cana-1285	7	37	often	often	ADV
cana-1285	7	38	requires	require	VERB
cana-1285	7	39	might	might	AUX
cana-1285	7	40	be	be	AUX
cana-1285	7	41	challenging	challenge	VERB
cana-1285	7	42	for	for	ADP
cana-1285	7	43	conventional	conventional	ADJ
cana-1285	7	44	analytical	analytical	ADJ
cana-1285	7	45	methods	method	NOUN
cana-1285	7	46	.	.	PUNCT
cana-1285	8	1	the	the	DET
cana-1285	8	2	intricate	intricate	ADJ
cana-1285	8	3	nonlinear	nonlinear	ADJ
cana-1285	8	4	patterns	pattern	NOUN
cana-1285	8	5	in	in	ADP
cana-1285	8	6	biological	biological	ADJ
cana-1285	8	7	data	datum	NOUN
cana-1285	8	8	could	could	AUX
cana-1285	8	9	be	be	AUX
cana-1285	8	10	difficult	difficult	ADJ
cana-1285	8	11	for	for	SCONJ
cana-1285	8	12	conventional	conventional	ADJ
cana-1285	8	13	biochemical	biochemical	ADJ
cana-1285	8	14	analysis	analysis	NOUN
cana-1285	8	15	methods	method	NOUN
cana-1285	8	16	to	to	PART
cana-1285	8	17	detect	detect	VERB
cana-1285	8	18	,	,	PUNCT
cana-1285	8	19	thereby	thereby	ADV
cana-1285	8	20	generating	generate	VERB
cana-1285	8	21	less	less	ADV
cana-1285	8	22	accurate	accurate	ADJ
cana-1285	8	23	predictions	prediction	NOUN
cana-1285	8	24	and	and	CCONJ
cana-1285	8	25	insights	insight	NOUN
cana-1285	8	26	.	.	PUNCT
cana-1285	9	1	this	this	DET
cana-1285	9	2	disparity	disparity	NOUN
cana-1285	9	3	highlights	highlight	VERB
cana-1285	9	4	the	the	DET
cana-1285	9	5	need	need	NOUN
cana-1285	9	6	of	of	ADP
cana-1285	9	7	more	more	ADV
cana-1285	9	8	robust	robust	ADJ
cana-1285	9	9	analytical	analytical	ADJ
cana-1285	9	10	techniques	technique	NOUN
cana-1285	9	11	able	able	ADJ
cana-1285	9	12	to	to	PART
cana-1285	9	13	effectively	effectively	ADV
cana-1285	9	14	model	model	VERB
cana-1285	9	15	and	and	CCONJ
cana-1285	9	16	grasp	grasp	VERB
cana-1285	9	17	these	these	DET
cana-1285	9	18	complexity	complexity	NOUN
cana-1285	9	19	.	.	PUNCT
cana-1285	10	1	we	we	PRON
cana-1285	10	2	suggest	suggest	VERB
cana-1285	10	3	to	to	PART
cana-1285	10	4	include	include	VERB
cana-1285	10	5	nonlinear	nonlinear	ADJ
cana-1285	10	6	dynamics	dynamic	NOUN
cana-1285	10	7	with	with	ADP
cana-1285	10	8	statistical	statistical	ADJ
cana-1285	10	9	approaches	approach	NOUN
cana-1285	10	10	inside	inside	ADP
cana-1285	10	11	deep	deep	ADJ
cana-1285	10	12	learning	learning	NOUN
cana-1285	10	13	models	model	NOUN
cana-1285	10	14	to	to	PART
cana-1285	10	15	raise	raise	VERB
cana-1285	10	16	the	the	DET
cana-1285	10	17	accuracy	accuracy	NOUN
cana-1285	10	18	and	and	CCONJ
cana-1285	10	19	interpretability	interpretability	NOUN
cana-1285	10	20	of	of	ADP
cana-1285	10	21	biochemical	biochemical	ADJ
cana-1285	10	22	component	component	NOUN
cana-1285	10	23	analysis	analysis	NOUN
cana-1285	10	24	.	.	PUNCT
cana-1285	11	1	we	we	PRON
cana-1285	11	2	especially	especially	ADV
cana-1285	11	3	incorporate	incorporate	VERB
cana-1285	11	4	nonlinear	nonlinear	ADJ
cana-1285	11	5	dynamic	dynamic	ADJ
cana-1285	11	6	systems	system	NOUN
cana-1285	11	7	theory	theory	NOUN
cana-1285	11	8	into	into	ADP
cana-1285	11	9	a	a	DET
cana-1285	11	10	deep	deep	ADJ
cana-1285	11	11	neural	neural	ADJ
cana-1285	11	12	network	network	NOUN
cana-1285	11	13	(	(	PUNCT
cana-1285	11	14	dnn	dnn	PROPN
cana-1285	11	15	)	)	PUNCT
cana-1285	11	16	architecture	architecture	NOUN
cana-1285	11	17	to	to	PART
cana-1285	11	18	raise	raise	VERB
cana-1285	11	19	the	the	DET
cana-1285	11	20	model	model	NOUN
cana-1285	11	21	's	's	PART
cana-1285	11	22	potential	potential	NOUN
cana-1285	11	23	to	to	PART
cana-1285	11	24	identify	identify	VERB
cana-1285	11	25	complex	complex	ADJ
cana-1285	11	26	temporal	temporal	ADJ
cana-1285	11	27	and	and	CCONJ
cana-1285	11	28	spatial	spatial	ADJ
cana-1285	11	29	patterns	pattern	NOUN
cana-1285	11	30	in	in	ADP
cana-1285	11	31	biochemical	biochemical	ADJ
cana-1285	11	32	datasets	dataset	NOUN
cana-1285	11	33	.	.	PUNCT
cana-1285	12	1	embedded	embed	VERB
cana-1285	12	2	into	into	ADP
cana-1285	12	3	the	the	DET
cana-1285	12	4	network	network	NOUN
cana-1285	12	5	design	design	NOUN
cana-1285	12	6	,	,	PUNCT
cana-1285	12	7	dynamic	dynamic	ADJ
cana-1285	12	8	system	system	NOUN
cana-1285	12	9	equations	equation	NOUN
cana-1285	12	10	are	be	AUX
cana-1285	12	11	applied	apply	VERB
cana-1285	12	12	in	in	ADP
cana-1285	12	13	statistical	statistical	ADJ
cana-1285	12	14	techniques	technique	NOUN
cana-1285	12	15	such	such	ADJ
cana-1285	12	16	variance	variance	NOUN
cana-1285	12	17	decomposition	decomposition	NOUN
cana-1285	12	18	and	and	CCONJ
cana-1285	12	19	regression	regression	VERB
cana-1285	12	20	analysis	analysis	NOUN
cana-1285	12	21	to	to	PART
cana-1285	12	22	enhance	enhance	VERB
cana-1285	12	23	model	model	NOUN
cana-1285	12	24	predictions	prediction	NOUN
cana-1285	12	25	.	.	PUNCT
cana-1285	13	1	the	the	DET
cana-1285	13	2	proposed	propose	VERB
cana-1285	13	3	method	method	NOUN
cana-1285	13	4	was	be	AUX
cana-1285	13	5	evaluated	evaluate	VERB
cana-1285	13	6	on	on	ADP
cana-1285	13	7	diverse	diverse	ADJ
cana-1285	13	8	sized	sized	ADJ
cana-1285	13	9	biochemical	biochemical	ADJ
cana-1285	13	10	datasets	dataset	NOUN
cana-1285	13	11	(	(	PUNCT
cana-1285	13	12	150,300	150,300	NUM
cana-1285	13	13	,	,	PUNCT
cana-1285	13	14	450	450	NUM
cana-1285	13	15	,	,	PUNCT
cana-1285	13	16	and	and	CCONJ
cana-1285	13	17	600	600	NUM
cana-1285	13	18	samples	sample	NOUN
cana-1285	13	19	)	)	PUNCT
cana-1285	13	20	.	.	PUNCT
cana-1285	14	1	with	with	ADP
cana-1285	14	2	a	a	DET
cana-1285	14	3	600	600	NUM
cana-1285	14	4	sample	sample	NOUN
cana-1285	14	5	dataset	dataset	NOUN
cana-1285	14	6	,	,	PUNCT
cana-1285	14	7	the	the	DET
cana-1285	14	8	combined	combined	ADJ
cana-1285	14	9	dnn	dnn	PROPN
cana-1285	14	10	model	model	NOUN
cana-1285	14	11	obtained	obtain	VERB
cana-1285	14	12	a	a	DET
cana-1285	14	13	prediction	prediction	NOUN
cana-1285	14	14	accuracy	accuracy	NOUN
cana-1285	14	15	of	of	ADP
cana-1285	14	16	92.5	92.5	NUM
cana-1285	14	17	%	%	NOUN
cana-1285	14	18	compared	compare	VERB
cana-1285	14	19	to	to	ADP
cana-1285	14	20	85.7	85.7	NUM
cana-1285	14	21	%	%	NOUN
cana-1285	14	22	with	with	ADP
cana-1285	14	23	conventional	conventional	ADJ
cana-1285	14	24	techniques	technique	NOUN
cana-1285	14	25	.	.	PUNCT
cana-1285	15	1	moreover	moreover	ADV
cana-1285	15	2	,	,	PUNCT
cana-1285	15	3	the	the	DET
cana-1285	15	4	improved	improved	ADJ
cana-1285	15	5	model	model	NOUN
cana-1285	15	6	interpretability	interpretability	NOUN
cana-1285	15	7	by	by	ADP
cana-1285	15	8	18	18	NUM
cana-1285	15	9	%	%	NOUN
cana-1285	15	10	found	find	VERB
cana-1285	15	11	by	by	ADP
cana-1285	15	12	variance	variance	NOUN
cana-1285	15	13	explained	explain	VERB
cana-1285	15	14	in	in	ADP
cana-1285	15	15	the	the	DET
cana-1285	15	16	biochemical	biochemical	ADJ
cana-1285	15	17	component	component	NOUN
cana-1285	15	18	analysis	analysis	NOUN
cana-1285	15	19	.	.	PUNCT
cana-1285	16	1	including	include	VERB
cana-1285	16	2	nonlinear	nonlinear	ADJ
cana-1285	16	3	dynamics	dynamic	NOUN
cana-1285	16	4	and	and	CCONJ
cana-1285	16	5	statistical	statistical	ADJ
cana-1285	16	6	methods	method	NOUN
cana-1285	16	7	clearly	clearly	ADV
cana-1285	16	8	improves	improve	VERB
cana-1285	16	9	the	the	DET
cana-1285	16	10	performance	performance	NOUN
cana-1285	16	11	and	and	CCONJ
cana-1285	16	12	interpretability	interpretability	NOUN
cana-1285	16	13	of	of	ADP
cana-1285	16	14	biochemical	biochemical	ADJ
cana-1285	16	15	component	component	NOUN
cana-1285	16	16	analysis	analysis	NOUN
cana-1285	16	17	models	model	NOUN
cana-1285	16	18	,	,	PUNCT
cana-1285	16	19	the	the	DET
cana-1285	16	20	results	result	NOUN
cana-1285	16	21	demonstrate	demonstrate	VERB
cana-1285	16	22	.	.	PUNCT
cana-1285	17	1	keywords	keyword	NOUN
cana-1285	17	2	:	:	PUNCT
cana-1285	17	3	nonlinear	nonlinear	ADJ
cana-1285	17	4	dynamics	dynamic	NOUN
cana-1285	17	5	,	,	PUNCT
cana-1285	17	6	deep	deep	ADJ
cana-1285	17	7	learning	learning	NOUN
cana-1285	17	8	,	,	PUNCT
cana-1285	17	9	biochemical	biochemical	ADJ
cana-1285	17	10	analysis	analysis	NOUN
cana-1285	17	11	,	,	PUNCT
cana-1285	17	12	statistical	statistical	ADJ
cana-1285	17	13	methods	method	NOUN
cana-1285	17	14	,	,	PUNCT
cana-1285	17	15	model	model	NOUN
cana-1285	17	16	integration	integration	NOUN
cana-1285	17	17	.	.	PUNCT
cana-1285	18	1	1	1	X
cana-1285	18	2	.	.	X
cana-1285	18	3	introduction	introduction	NOUN
cana-1285	18	4	in	in	ADP
cana-1285	18	5	many	many	ADJ
cana-1285	18	6	different	different	ADJ
cana-1285	18	7	scientific	scientific	ADJ
cana-1285	18	8	fields	field	NOUN
cana-1285	18	9	,	,	PUNCT
cana-1285	18	10	including	include	VERB
cana-1285	18	11	biochemical	biochemical	ADJ
cana-1285	18	12	research	research	NOUN
cana-1285	18	13	[	[	X
cana-1285	18	14	1	1	X
cana-1285	18	15	]	]	PUNCT
cana-1285	18	16	recently	recently	ADV
cana-1285	18	17	,	,	PUNCT
cana-1285	18	18	deep	deep	ADJ
cana-1285	18	19	learning	learning	NOUN
cana-1285	18	20	coupled	couple	VERB
cana-1285	18	21	with	with	ADP
cana-1285	18	22	nonlinear	nonlinear	ADJ
cana-1285	18	23	dynamics	dynamic	NOUN
cana-1285	18	24	has	have	AUX
cana-1285	18	25	become	become	VERB
cana-1285	18	26	a	a	DET
cana-1285	18	27	powerful	powerful	ADJ
cana-1285	18	28	tool	tool	NOUN
cana-1285	18	29	for	for	ADP
cana-1285	18	30	handling	handle	VERB
cana-1285	18	31	difficult	difficult	ADJ
cana-1285	18	32	problems	problem	NOUN
cana-1285	18	33	.	.	PUNCT
cana-1285	19	1	many	many	ADJ
cana-1285	19	2	times	time	NOUN
cana-1285	19	3	involving	involve	VERB
cana-1285	19	4	complicated	complicated	ADJ
cana-1285	19	5	interactions	interaction	NOUN
cana-1285	19	6	between	between	ADP
cana-1285	19	7	numerous	numerous	ADJ
cana-1285	19	8	components	component	NOUN
cana-1285	19	9	,	,	PUNCT
cana-1285	19	10	nonlinear	nonlinear	ADJ
cana-1285	19	11	linkages	linkage	NOUN
cana-1285	19	12	and	and	CCONJ
cana-1285	19	13	temporal	temporal	ADJ
cana-1285	19	14	communications	communication	NOUN
cana-1285	19	15	on	on	ADP
cana-1285	19	16	applied	apply	VERB
cana-1285	19	17	nonlinear	nonlinear	ADJ
cana-1285	19	18	analysis	analysis	NOUN
cana-1285	19	19	issn	issn	NOUN
cana-1285	19	20	:	:	PUNCT
cana-1285	19	21	1074	1074	NUM
cana-1285	19	22	-	-	PUNCT
cana-1285	19	23	133x	133x	NUM
cana-1285	19	24	vol	vol	NOUN
cana-1285	19	25	31	31	NUM
cana-1285	19	26	no	no	NOUN
cana-1285	19	27	.	.	PUNCT
cana-1285	20	1	7s	7	NOUN
cana-1285	20	2	(	(	PUNCT
cana-1285	20	3	2024	2024	NUM
cana-1285	20	4	)	)	PUNCT
cana-1285	20	5	68	68	NUM
cana-1285	20	6	https://internationalpubls.com	https://internationalpubls.com	NUM
cana-1285	20	7	dependencies	dependency	NOUN
cana-1285	20	8	describe	describe	VERB
cana-1285	20	9	biological	biological	ADJ
cana-1285	20	10	processes	process	NOUN
cana-1285	20	11	[	[	X
cana-1285	20	12	2	2	NUM
cana-1285	20	13	]	]	PUNCT
cana-1285	20	14	.	.	PUNCT
cana-1285	21	1	conventional	conventional	ADJ
cana-1285	21	2	modeling	modeling	NOUN
cana-1285	21	3	approaches	approach	NOUN
cana-1285	21	4	may	may	AUX
cana-1285	21	5	find	find	VERB
cana-1285	21	6	it	it	PRON
cana-1285	21	7	challenging	challenge	VERB
cana-1285	21	8	to	to	PART
cana-1285	21	9	effectively	effectively	ADV
cana-1285	21	10	represent	represent	VERB
cana-1285	21	11	these	these	DET
cana-1285	21	12	complexities	complexity	NOUN
cana-1285	21	13	;	;	PUNCT
cana-1285	21	14	so	so	CCONJ
cana-1285	21	15	,	,	PUNCT
cana-1285	21	16	fewer	few	ADJ
cana-1285	21	17	than	than	ADP
cana-1285	21	18	perfect	perfect	ADJ
cana-1285	21	19	projections	projection	NOUN
cana-1285	21	20	and	and	CCONJ
cana-1285	21	21	insights	insight	NOUN
cana-1285	21	22	follow	follow	VERB
cana-1285	21	23	[	[	X
cana-1285	21	24	3	3	NUM
cana-1285	21	25	]	]	PUNCT
cana-1285	21	26	.	.	PUNCT
cana-1285	22	1	since	since	SCONJ
cana-1285	22	2	deep	deep	ADJ
cana-1285	22	3	learning	learning	NOUN
cana-1285	22	4	can	can	AUX
cana-1285	22	5	repeat	repeat	VERB
cana-1285	22	6	complex	complex	ADJ
cana-1285	22	7	tasks	task	NOUN
cana-1285	22	8	over	over	ADP
cana-1285	22	9	numerous	numerous	ADJ
cana-1285	22	10	layers	layer	NOUN
cana-1285	22	11	and	and	CCONJ
cana-1285	22	12	parameters	parameter	NOUN
cana-1285	22	13	,	,	PUNCT
cana-1285	22	14	it	it	PRON
cana-1285	22	15	has	have	AUX
cana-1285	22	16	shown	show	VERB
cana-1285	22	17	promise	promise	NOUN
cana-1285	22	18	in	in	ADP
cana-1285	22	19	addressing	address	VERB
cana-1285	22	20	such	such	ADJ
cana-1285	22	21	challenges	challenge	NOUN
cana-1285	22	22	[	[	X
cana-1285	22	23	4	4	NUM
cana-1285	22	24	]	]	PUNCT
cana-1285	22	25	.	.	PUNCT
cana-1285	23	1	still	still	ADV
cana-1285	23	2	,	,	PUNCT
cana-1285	23	3	the	the	DET
cana-1285	23	4	relatively	relatively	ADV
cana-1285	23	5	new	new	ADJ
cana-1285	23	6	and	and	CCONJ
cana-1285	23	7	growing	grow	VERB
cana-1285	23	8	discipline	discipline	NOUN
cana-1285	23	9	of	of	ADP
cana-1285	23	10	including	include	VERB
cana-1285	23	11	nonlinear	nonlinear	ADJ
cana-1285	23	12	dynamics	dynamic	NOUN
cana-1285	23	13	into	into	ADP
cana-1285	23	14	deep	deep	ADJ
cana-1285	23	15	learning	learning	NOUN
cana-1285	23	16	models	model	NOUN
cana-1285	23	17	offers	offer	VERB
cana-1285	23	18	the	the	DET
cana-1285	23	19	means	mean	NOUN
cana-1285	23	20	for	for	ADP
cana-1285	23	21	more	more	ADV
cana-1285	23	22	accurate	accurate	ADJ
cana-1285	23	23	and	and	CCONJ
cana-1285	23	24	interpretable	interpretable	ADJ
cana-1285	23	25	study	study	NOUN
cana-1285	23	26	of	of	ADP
cana-1285	23	27	biological	biological	ADJ
cana-1285	23	28	data	datum	NOUN
cana-1285	23	29	[	[	X
cana-1285	23	30	5	5	NUM
cana-1285	23	31	]	]	PUNCT
cana-1285	23	32	.	.	PUNCT
cana-1285	24	1	the	the	DET
cana-1285	24	2	main	main	ADJ
cana-1285	24	3	challenges	challenge	NOUN
cana-1285	24	4	in	in	ADP
cana-1285	24	5	biochemical	biochemical	ADJ
cana-1285	24	6	component	component	NOUN
cana-1285	24	7	analysis	analysis	NOUN
cana-1285	24	8	are	be	AUX
cana-1285	24	9	in	in	ADP
cana-1285	24	10	capture	capture	NOUN
cana-1285	24	11	of	of	ADP
cana-1285	24	12	the	the	DET
cana-1285	24	13	dynamic	dynamic	ADJ
cana-1285	24	14	behavior	behavior	NOUN
cana-1285	24	15	of	of	ADP
cana-1285	24	16	biochemical	biochemical	ADJ
cana-1285	24	17	systems	system	NOUN
cana-1285	24	18	,	,	PUNCT
cana-1285	24	19	handling	handling	NOUN
cana-1285	24	20	of	of	ADP
cana-1285	24	21	high	high	ADJ
cana-1285	24	22	-	-	PUNCT
cana-1285	24	23	dimensional	dimensional	ADJ
cana-1285	24	24	data	datum	NOUN
cana-1285	24	25	,	,	PUNCT
cana-1285	24	26	and	and	CCONJ
cana-1285	24	27	assurance	assurance	NOUN
cana-1285	24	28	of	of	ADP
cana-1285	24	29	reliable	reliable	ADJ
cana-1285	24	30	and	and	CCONJ
cana-1285	24	31	interpretable	interpretable	ADJ
cana-1285	24	32	predictive	predictive	ADJ
cana-1285	24	33	models	model	NOUN
cana-1285	24	34	[	[	X
cana-1285	24	35	6	6	NUM
cana-1285	24	36	]	]	PUNCT
cana-1285	24	37	.	.	PUNCT
cana-1285	25	1	nonlinear	nonlinear	ADJ
cana-1285	25	2	dynamics	dynamic	NOUN
cana-1285	25	3	adds	add	VERB
cana-1285	25	4	considerably	considerably	ADV
cana-1285	25	5	more	more	ADJ
cana-1285	25	6	difficulty	difficulty	NOUN
cana-1285	25	7	since	since	SCONJ
cana-1285	25	8	it	it	PRON
cana-1285	25	9	demands	demand	VERB
cana-1285	25	10	solving	solve	VERB
cana-1285	25	11	differential	differential	ADJ
cana-1285	25	12	equations	equation	NOUN
cana-1285	25	13	defining	define	VERB
cana-1285	25	14	the	the	DET
cana-1285	25	15	temporal	temporal	ADJ
cana-1285	25	16	development	development	NOUN
cana-1285	25	17	of	of	ADP
cana-1285	25	18	system	system	NOUN
cana-1285	25	19	states	state	NOUN
cana-1285	25	20	.	.	PUNCT
cana-1285	26	1	although	although	SCONJ
cana-1285	26	2	maintaining	maintain	VERB
cana-1285	26	3	their	their	PRON
cana-1285	26	4	scalability	scalability	NOUN
cana-1285	26	5	and	and	CCONJ
cana-1285	26	6	speed	speed	NOUN
cana-1285	26	7	,	,	PUNCT
cana-1285	26	8	this	this	DET
cana-1285	26	9	complexity	complexity	NOUN
cana-1285	26	10	could	could	AUX
cana-1285	26	11	make	make	VERB
cana-1285	26	12	it	it	PRON
cana-1285	26	13	difficult	difficult	ADJ
cana-1285	26	14	to	to	PART
cana-1285	26	15	integrate	integrate	VERB
cana-1285	26	16	dynamic	dynamic	ADJ
cana-1285	26	17	constraints	constraint	NOUN
cana-1285	26	18	into	into	ADP
cana-1285	26	19	deep	deep	ADJ
cana-1285	26	20	learning	learning	NOUN
cana-1285	26	21	models	model	NOUN
cana-1285	26	22	[	[	X
cana-1285	26	23	7	7	NUM
cana-1285	26	24	]	]	PUNCT
cana-1285	26	25	.	.	PUNCT
cana-1285	27	1	furthermore	furthermore	ADV
cana-1285	27	2	,	,	PUNCT
cana-1285	27	3	present	present	ADJ
cana-1285	27	4	models	model	NOUN
cana-1285	27	5	could	could	AUX
cana-1285	27	6	not	not	PART
cana-1285	27	7	be	be	AUX
cana-1285	27	8	able	able	ADJ
cana-1285	27	9	to	to	PART
cana-1285	27	10	fully	fully	ADV
cana-1285	27	11	use	use	VERB
cana-1285	27	12	dynamic	dynamic	ADJ
cana-1285	27	13	information	information	NOUN
cana-1285	27	14	,	,	PUNCT
cana-1285	27	15	which	which	PRON
cana-1285	27	16	would	would	AUX
cana-1285	27	17	make	make	VERB
cana-1285	27	18	exact	exact	ADJ
cana-1285	27	19	prediction	prediction	NOUN
cana-1285	27	20	of	of	ADP
cana-1285	27	21	interactions	interaction	NOUN
cana-1285	27	22	and	and	CCONJ
cana-1285	27	23	behavior	behavior	NOUN
cana-1285	27	24	of	of	ADP
cana-1285	27	25	biological	biological	ADJ
cana-1285	27	26	components	component	NOUN
cana-1285	27	27	challenging	challenge	VERB
cana-1285	27	28	[	[	X
cana-1285	27	29	8	8	NUM
cana-1285	27	30	]	]	PUNCT
cana-1285	27	31	.	.	PUNCT
cana-1285	28	1	this	this	DET
cana-1285	28	2	work	work	NOUN
cana-1285	28	3	tackles	tackle	NOUN
cana-1285	28	4	is	be	AUX
cana-1285	28	5	the	the	DET
cana-1285	28	6	need	need	NOUN
cana-1285	28	7	of	of	ADP
cana-1285	28	8	a	a	DET
cana-1285	28	9	deep	deep	ADJ
cana-1285	28	10	learning	learning	NOUN
cana-1285	28	11	model	model	NOUN
cana-1285	28	12	that	that	PRON
cana-1285	28	13	effectively	effectively	ADV
cana-1285	28	14	incorporates	incorporate	VERB
cana-1285	28	15	nonlinear	nonlinear	ADJ
cana-1285	28	16	dynamics	dynamic	NOUN
cana-1285	28	17	to	to	PART
cana-1285	28	18	increase	increase	VERB
cana-1285	28	19	the	the	DET
cana-1285	28	20	accuracy	accuracy	NOUN
cana-1285	28	21	and	and	CCONJ
cana-1285	28	22	interpretability	interpretability	NOUN
cana-1285	28	23	of	of	ADP
cana-1285	28	24	biochemical	biochemical	ADJ
cana-1285	28	25	component	component	NOUN
cana-1285	28	26	analysis	analysis	NOUN
cana-1285	28	27	[	[	X
cana-1285	28	28	9	9	NUM
cana-1285	28	29	]	]	PUNCT
cana-1285	28	30	.	.	PUNCT
cana-1285	29	1	the	the	DET
cana-1285	29	2	objective	objective	NOUN
cana-1285	29	3	is	be	AUX
cana-1285	29	4	especially	especially	ADV
cana-1285	29	5	to	to	PART
cana-1285	29	6	develop	develop	VERB
cana-1285	29	7	a	a	DET
cana-1285	29	8	model	model	NOUN
cana-1285	29	9	that	that	SCONJ
cana-1285	29	10	not	not	PART
cana-1285	29	11	only	only	ADV
cana-1285	29	12	highly	highly	ADV
cana-1285	29	13	accurate	accurate	ADJ
cana-1285	29	14	prediction	prediction	NOUN
cana-1285	29	15	of	of	ADP
cana-1285	29	16	biochemical	biochemical	ADJ
cana-1285	29	17	consequences	consequence	NOUN
cana-1285	29	18	but	but	CCONJ
cana-1285	29	19	also	also	ADV
cana-1285	29	20	conforms	conform	VERB
cana-1285	29	21	to	to	ADP
cana-1285	29	22	the	the	DET
cana-1285	29	23	basic	basic	ADJ
cana-1285	29	24	dynamic	dynamic	ADJ
cana-1285	29	25	constraints	constraint	NOUN
cana-1285	29	26	of	of	ADP
cana-1285	29	27	the	the	DET
cana-1285	29	28	biochemical	biochemical	ADJ
cana-1285	29	29	system	system	NOUN
cana-1285	29	30	[	[	X
cana-1285	29	31	10	10	NUM
cana-1285	29	32	]	]	PUNCT
cana-1285	29	33	.	.	PUNCT
cana-1285	30	1	this	this	PRON
cana-1285	30	2	implies	imply	VERB
cana-1285	30	3	creating	create	VERB
cana-1285	30	4	a	a	DET
cana-1285	30	5	deep	deep	ADJ
cana-1285	30	6	learning	learning	NOUN
cana-1285	30	7	framework	framework	NOUN
cana-1285	30	8	that	that	PRON
cana-1285	30	9	captures	capture	VERB
cana-1285	30	10	complex	complex	ADJ
cana-1285	30	11	temporal	temporal	ADJ
cana-1285	30	12	and	and	CCONJ
cana-1285	30	13	spatial	spatial	ADJ
cana-1285	30	14	relationships	relationship	NOUN
cana-1285	30	15	yet	yet	ADV
cana-1285	30	16	ensures	ensure	VERB
cana-1285	30	17	the	the	DET
cana-1285	30	18	predictions	prediction	NOUN
cana-1285	30	19	correspond	correspond	VERB
cana-1285	30	20	with	with	ADP
cana-1285	30	21	the	the	DET
cana-1285	30	22	behavior	behavior	NOUN
cana-1285	30	23	of	of	ADP
cana-1285	30	24	the	the	DET
cana-1285	30	25	dynamic	dynamic	ADJ
cana-1285	30	26	system	system	NOUN
cana-1285	30	27	[	[	X
cana-1285	30	28	11	11	NUM
cana-1285	30	29	]	]	PUNCT
cana-1285	30	30	.	.	PUNCT
cana-1285	31	1	the	the	DET
cana-1285	31	2	primary	primary	ADJ
cana-1285	31	3	objectives	objective	NOUN
cana-1285	31	4	of	of	ADP
cana-1285	31	5	this	this	DET
cana-1285	31	6	research	research	NOUN
cana-1285	31	7	are	be	AUX
cana-1285	31	8	:	:	PUNCT
cana-1285	31	9	1	1	X
cana-1285	31	10	.	.	PUNCT
cana-1285	31	11	to	to	PART
cana-1285	31	12	construct	construct	VERB
cana-1285	31	13	a	a	DET
cana-1285	31	14	deep	deep	ADJ
cana-1285	31	15	learning	learning	NOUN
cana-1285	31	16	model	model	NOUN
cana-1285	31	17	with	with	ADP
cana-1285	31	18	nonlinear	nonlinear	ADJ
cana-1285	31	19	dynamics	dynamic	NOUN
cana-1285	31	20	including	include	VERB
cana-1285	31	21	into	into	ADP
cana-1285	31	22	consideration	consideration	NOUN
cana-1285	31	23	better	well	ADJ
cana-1285	31	24	biochemical	biochemical	ADJ
cana-1285	31	25	component	component	NOUN
cana-1285	31	26	analysis	analysis	NOUN
cana-1285	31	27	.	.	PUNCT
cana-1285	32	1	2	2	X
cana-1285	32	2	.	.	PUNCT
cana-1285	32	3	to	to	PART
cana-1285	32	4	evaluate	evaluate	VERB
cana-1285	32	5	the	the	DET
cana-1285	32	6	performance	performance	NOUN
cana-1285	32	7	of	of	ADP
cana-1285	32	8	the	the	DET
cana-1285	32	9	suggested	suggest	VERB
cana-1285	32	10	model	model	NOUN
cana-1285	32	11	in	in	ADP
cana-1285	32	12	respect	respect	NOUN
cana-1285	32	13	to	to	ADP
cana-1285	32	14	computing	compute	VERB
cana-1285	32	15	economy	economy	NOUN
cana-1285	32	16	,	,	PUNCT
cana-1285	32	17	interpretability	interpretability	NOUN
cana-1285	32	18	,	,	PUNCT
cana-1285	32	19	and	and	CCONJ
cana-1285	32	20	prediction	prediction	NOUN
cana-1285	32	21	accuracy	accuracy	NOUN
cana-1285	32	22	.	.	PUNCT
cana-1285	33	1	3	3	X
cana-1285	33	2	.	.	X
cana-1285	33	3	the	the	DET
cana-1285	33	4	proposed	propose	VERB
cana-1285	33	5	model	model	NOUN
cana-1285	33	6	will	will	AUX
cana-1285	33	7	be	be	AUX
cana-1285	33	8	compared	compare	VERB
cana-1285	33	9	with	with	ADP
cana-1285	33	10	existing	exist	VERB
cana-1285	33	11	in	in	ADP
cana-1285	33	12	use	use	NOUN
cana-1285	33	13	approaches	approach	NOUN
cana-1285	33	14	as	as	ADP
cana-1285	33	15	unidl4biopep	unidl4biopep	PROPN
cana-1285	33	16	,	,	PUNCT
cana-1285	33	17	neural	neural	ADJ
cana-1285	33	18	network	network	NOUN
cana-1285	33	19	language	language	NOUN
cana-1285	33	20	model	model	NOUN
cana-1285	33	21	(	(	PUNCT
cana-1285	33	22	nnlm	nnlm	PROPN
cana-1285	33	23	)	)	PUNCT
cana-1285	33	24	,	,	PUNCT
cana-1285	33	25	and	and	CCONJ
cana-1285	33	26	deepbio	deepbio	NOUN
cana-1285	33	27	to	to	PART
cana-1285	33	28	demonstrate	demonstrate	VERB
cana-1285	33	29	its	its	PRON
cana-1285	33	30	advantages	advantage	NOUN
cana-1285	33	31	and	and	CCONJ
cana-1285	33	32	efficiency	efficiency	NOUN
cana-1285	33	33	.	.	PUNCT
cana-1285	34	1	this	this	DET
cana-1285	34	2	approach	approach	NOUN
cana-1285	34	3	is	be	AUX
cana-1285	34	4	a	a	DET
cana-1285	34	5	novel	novel	ADJ
cana-1285	34	6	combination	combination	NOUN
cana-1285	34	7	for	for	ADP
cana-1285	34	8	biological	biological	ADJ
cana-1285	34	9	study	study	NOUN
cana-1285	34	10	of	of	ADP
cana-1285	34	11	nonlinear	nonlinear	ADJ
cana-1285	34	12	dynamics	dynamic	NOUN
cana-1285	34	13	using	use	VERB
cana-1285	34	14	deep	deep	ADJ
cana-1285	34	15	learning	learning	NOUN
cana-1285	34	16	algorithms	algorithm	NOUN
cana-1285	34	17	.	.	PUNCT
cana-1285	35	1	although	although	SCONJ
cana-1285	35	2	standard	standard	ADJ
cana-1285	35	3	deep	deep	ADJ
cana-1285	35	4	learning	learning	NOUN
cana-1285	35	5	models	model	NOUN
cana-1285	35	6	largely	largely	ADV
cana-1285	35	7	focus	focus	VERB
cana-1285	35	8	on	on	ADP
cana-1285	35	9	prediction	prediction	NOUN
cana-1285	35	10	accuracy	accuracy	NOUN
cana-1285	35	11	,	,	PUNCT
cana-1285	35	12	the	the	DET
cana-1285	35	13	proposed	propose	VERB
cana-1285	35	14	method	method	NOUN
cana-1285	35	15	includes	include	VERB
cana-1285	35	16	dynamic	dynamic	ADJ
cana-1285	35	17	restrictions	restriction	NOUN
cana-1285	35	18	straight	straight	ADV
cana-1285	35	19	into	into	ADP
cana-1285	35	20	the	the	DET
cana-1285	35	21	architecture	architecture	NOUN
cana-1285	35	22	and	and	CCONJ
cana-1285	35	23	loss	loss	NOUN
cana-1285	35	24	function	function	NOUN
cana-1285	35	25	of	of	ADP
cana-1285	35	26	the	the	DET
cana-1285	35	27	model	model	NOUN
cana-1285	35	28	.	.	PUNCT
cana-1285	36	1	by	by	ADP
cana-1285	36	2	means	mean	NOUN
cana-1285	36	3	of	of	ADP
cana-1285	36	4	this	this	DET
cana-1285	36	5	integration	integration	NOUN
cana-1285	36	6	,	,	PUNCT
cana-1285	36	7	the	the	DET
cana-1285	36	8	model	model	NOUN
cana-1285	36	9	reflects	reflect	VERB
cana-1285	36	10	complex	complex	ADJ
cana-1285	36	11	temporal	temporal	ADJ
cana-1285	36	12	dependencies	dependency	NOUN
cana-1285	36	13	and	and	CCONJ
cana-1285	36	14	interactions	interaction	NOUN
cana-1285	36	15	inherent	inherent	ADJ
cana-1285	36	16	in	in	ADP
cana-1285	36	17	biochemical	biochemical	ADJ
cana-1285	36	18	systems	system	NOUN
cana-1285	36	19	,	,	PUNCT
cana-1285	36	20	hence	hence	ADV
cana-1285	36	21	producing	produce	VERB
cana-1285	36	22	more	more	ADV
cana-1285	36	23	accurate	accurate	ADJ
cana-1285	36	24	and	and	CCONJ
cana-1285	36	25	interpretable	interpretable	ADJ
cana-1285	36	26	predictions	prediction	NOUN
cana-1285	36	27	.	.	PUNCT
cana-1285	37	1	moreover	moreover	ADV
cana-1285	37	2	special	special	ADJ
cana-1285	37	3	from	from	ADP
cana-1285	37	4	present	present	ADJ
cana-1285	37	5	methods	method	NOUN
cana-1285	37	6	is	be	AUX
cana-1285	37	7	the	the	DET
cana-1285	37	8	proposed	propose	VERB
cana-1285	37	9	model	model	NOUN
cana-1285	37	10	,	,	PUNCT
cana-1285	37	11	which	which	PRON
cana-1285	37	12	makes	make	VERB
cana-1285	37	13	use	use	NOUN
cana-1285	37	14	of	of	ADP
cana-1285	37	15	new	new	ADJ
cana-1285	37	16	nonlinear	nonlinear	ADJ
cana-1285	37	17	differential	differential	ADJ
cana-1285	37	18	equations	equation	NOUN
cana-1285	37	19	adapted	adapt	VERB
cana-1285	37	20	to	to	ADP
cana-1285	37	21	the	the	DET
cana-1285	37	22	specific	specific	ADJ
cana-1285	37	23	dynamics	dynamic	NOUN
cana-1285	37	24	of	of	ADP
cana-1285	37	25	biological	biological	ADJ
cana-1285	37	26	processes	process	NOUN
cana-1285	37	27	.	.	PUNCT
cana-1285	38	1	this	this	DET
cana-1285	38	2	research	research	NOUN
cana-1285	38	3	makes	make	VERB
cana-1285	38	4	several	several	ADJ
cana-1285	38	5	key	key	ADJ
cana-1285	38	6	contributions	contribution	NOUN
cana-1285	38	7	:	:	PUNCT
cana-1285	38	8	1	1	X
cana-1285	38	9	.	.	PUNCT
cana-1285	38	10	by	by	ADP
cana-1285	38	11	including	include	VERB
cana-1285	38	12	nonlinear	nonlinear	ADJ
cana-1285	38	13	dynamics	dynamic	NOUN
cana-1285	38	14	using	use	VERB
cana-1285	38	15	modified	modify	VERB
cana-1285	38	16	differential	differential	ADJ
cana-1285	38	17	equations	equation	NOUN
cana-1285	38	18	,	,	PUNCT
cana-1285	38	19	a	a	DET
cana-1285	38	20	deep	deep	ADJ
cana-1285	38	21	learning	learning	NOUN
cana-1285	38	22	model	model	NOUN
cana-1285	38	23	improves	improve	VERB
cana-1285	38	24	its	its	PRON
cana-1285	38	25	capacity	capacity	NOUN
cana-1285	38	26	to	to	PART
cana-1285	38	27	reflect	reflect	VERB
cana-1285	38	28	complex	complex	ADJ
cana-1285	38	29	biological	biological	ADJ
cana-1285	38	30	interactions	interaction	NOUN
cana-1285	38	31	.	.	PUNCT
cana-1285	39	1	communications	communication	NOUN
cana-1285	39	2	on	on	ADP
cana-1285	39	3	applied	apply	VERB
cana-1285	39	4	nonlinear	nonlinear	ADJ
cana-1285	39	5	analysis	analysis	NOUN
cana-1285	39	6	issn	issn	NOUN
cana-1285	39	7	:	:	PUNCT
cana-1285	39	8	1074	1074	NUM
cana-1285	39	9	-	-	PUNCT
cana-1285	39	10	133x	133x	NUM
cana-1285	39	11	vol	vol	NOUN
cana-1285	39	12	31	31	NUM
cana-1285	39	13	no	no	NOUN
cana-1285	39	14	.	.	PUNCT
cana-1285	40	1	7s	7	NOUN
cana-1285	40	2	(	(	PUNCT
cana-1285	40	3	2024	2024	NUM
cana-1285	40	4	)	)	PUNCT
cana-1285	40	5	69	69	NUM
cana-1285	40	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1285	40	7	2	2	NUM
cana-1285	40	8	.	.	PUNCT
cana-1285	40	9	emphasizing	emphasize	VERB
cana-1285	40	10	its	its	PRON
cana-1285	40	11	improved	improved	ADJ
cana-1285	40	12	accuracy	accuracy	NOUN
cana-1285	40	13	,	,	PUNCT
cana-1285	40	14	interpretability	interpretability	NOUN
cana-1285	40	15	,	,	PUNCT
cana-1285	40	16	and	and	CCONJ
cana-1285	40	17	efficiency	efficiency	NOUN
cana-1285	40	18	,	,	PUNCT
cana-1285	40	19	a	a	DET
cana-1285	40	20	comprehensive	comprehensive	ADJ
cana-1285	40	21	performance	performance	NOUN
cana-1285	40	22	analysis	analysis	NOUN
cana-1285	40	23	comparing	compare	VERB
cana-1285	40	24	the	the	DET
cana-1285	40	25	proposed	propose	VERB
cana-1285	40	26	model	model	NOUN
cana-1285	40	27	with	with	ADP
cana-1285	40	28	present	present	ADJ
cana-1285	40	29	methods	method	NOUN
cana-1285	40	30	3	3	NUM
cana-1285	40	31	.	.	PUNCT
cana-1285	40	32	dynamic	dynamic	ADJ
cana-1285	40	33	limitations	limitation	NOUN
cana-1285	40	34	allow	allow	VERB
cana-1285	40	35	one	one	PRON
cana-1285	40	36	to	to	PART
cana-1285	40	37	better	well	ADV
cana-1285	40	38	understand	understand	VERB
cana-1285	40	39	biological	biological	ADJ
cana-1285	40	40	processes	process	NOUN
cana-1285	40	41	,	,	PUNCT
cana-1285	40	42	hence	hence	ADV
cana-1285	40	43	improving	improve	VERB
cana-1285	40	44	the	the	DET
cana-1285	40	45	knowledge	knowledge	NOUN
cana-1285	40	46	of	of	ADP
cana-1285	40	47	basic	basic	ADJ
cana-1285	40	48	system	system	NOUN
cana-1285	40	49	interactions	interaction	NOUN
cana-1285	40	50	and	and	CCONJ
cana-1285	40	51	behaviors	behavior	NOUN
cana-1285	40	52	.	.	PUNCT
cana-1285	41	1	2	2	X
cana-1285	41	2	.	.	NUM
cana-1285	41	3	related	relate	VERB
cana-1285	41	4	works	work	NOUN
cana-1285	41	5	in	in	ADP
cana-1285	41	6	high	high	ADJ
cana-1285	41	7	-	-	PUNCT
cana-1285	41	8	throughput	throughput	NOUN
cana-1285	41	9	biological	biological	ADJ
cana-1285	41	10	sequence	sequence	NOUN
cana-1285	41	11	analysis	analysis	NOUN
cana-1285	41	12	and	and	CCONJ
cana-1285	41	13	cancer	cancer	NOUN
cana-1285	41	14	diagnosis	diagnosis	NOUN
cana-1285	41	15	,	,	PUNCT
cana-1285	41	16	recent	recent	ADJ
cana-1285	41	17	advances	advance	NOUN
cana-1285	41	18	in	in	ADP
cana-1285	41	19	deep	deep	ADJ
cana-1285	41	20	learning	learning	NOUN
cana-1285	41	21	and	and	CCONJ
cana-1285	41	22	computational	computational	ADJ
cana-1285	41	23	methods	method	NOUN
cana-1285	41	24	have	have	AUX
cana-1285	41	25	significantly	significantly	ADV
cana-1285	41	26	impacted	impact	VERB
cana-1285	41	27	many	many	ADJ
cana-1285	41	28	sectors	sector	NOUN
cana-1285	41	29	of	of	ADP
cana-1285	41	30	biological	biological	ADJ
cana-1285	41	31	and	and	CCONJ
cana-1285	41	32	biomedical	biomedical	ADJ
cana-1285	41	33	research	research	NOUN
cana-1285	41	34	.	.	PUNCT
cana-1285	42	1	designed	design	VERB
cana-1285	42	2	to	to	PART
cana-1285	42	3	address	address	VERB
cana-1285	42	4	challenges	challenge	NOUN
cana-1285	42	5	in	in	ADP
cana-1285	42	6	various	various	ADJ
cana-1285	42	7	domains	domain	NOUN
cana-1285	42	8	,	,	PUNCT
cana-1285	42	9	several	several	ADJ
cana-1285	42	10	state	state	NOUN
cana-1285	42	11	-	-	PUNCT
cana-1285	42	12	ofthe	ofthe	NOUN
cana-1285	42	13	-	-	PUNCT
cana-1285	42	14	art	art	NOUN
cana-1285	42	15	techniques	technique	NOUN
cana-1285	42	16	and	and	CCONJ
cana-1285	42	17	platforms	platform	NOUN
cana-1285	42	18	each	each	PRON
cana-1285	42	19	contribute	contribute	VERB
cana-1285	42	20	in	in	ADP
cana-1285	42	21	distinct	distinct	ADJ
cana-1285	42	22	ways	way	NOUN
cana-1285	42	23	to	to	PART
cana-1285	42	24	progress	progress	VERB
cana-1285	42	25	computational	computational	ADJ
cana-1285	42	26	biology	biology	NOUN
cana-1285	42	27	.	.	PUNCT
cana-1285	43	1	deepbio	deepbio	PROPN
cana-1285	43	2	is	be	AUX
cana-1285	43	3	pioneering	pioneer	VERB
cana-1285	43	4	in	in	ADP
cana-1285	43	5	the	the	DET
cana-1285	43	6	field	field	NOUN
cana-1285	43	7	of	of	ADP
cana-1285	43	8	deep	deep	ADJ
cana-1285	43	9	learning	learning	NOUN
cana-1285	43	10	for	for	ADP
cana-1285	43	11	biological	biological	ADJ
cana-1285	43	12	sequence	sequence	NOUN
cana-1285	43	13	functional	functional	ADJ
cana-1285	43	14	analysis	analysis	NOUN
cana-1285	43	15	.	.	PUNCT
cana-1285	44	1	deepbio	deepbio	NOUN
cana-1285	44	2	,	,	PUNCT
cana-1285	44	3	as	as	SCONJ
cana-1285	44	4	described	describe	VERB
cana-1285	44	5	in	in	ADP
cana-1285	44	6	[	[	X
cana-1285	44	7	12	12	NUM
cana-1285	44	8	]	]	PUNCT
cana-1285	44	9	is	be	AUX
cana-1285	44	10	an	an	DET
cana-1285	44	11	automated	automate	VERB
cana-1285	44	12	,	,	PUNCT
cana-1285	44	13	interpretable	interpretable	ADJ
cana-1285	44	14	deep	deep	ADJ
cana-1285	44	15	-	-	PUNCT
cana-1285	44	16	learning	learn	VERB
cana-1285	44	17	method	method	NOUN
cana-1285	44	18	allowing	allow	VERB
cana-1285	44	19	highthroughput	highthroughput	VERB
cana-1285	44	20	biological	biological	ADJ
cana-1285	44	21	sequence	sequence	NOUN
cana-1285	44	22	analysis	analysis	NOUN
cana-1285	44	23	.	.	PUNCT
cana-1285	45	1	its	its	PRON
cana-1285	45	2	42	42	NUM
cana-1285	45	3	advanced	advanced	ADJ
cana-1285	45	4	deep	deep	ADJ
cana-1285	45	5	-	-	PUNCT
cana-1285	45	6	learning	learn	VERB
cana-1285	45	7	algorithms	algorithm	NOUN
cana-1285	45	8	let	let	VERB
cana-1285	45	9	users	user	NOUN
cana-1285	45	10	evaluate	evaluate	VERB
cana-1285	45	11	,	,	PUNCT
cana-1285	45	12	train	train	NOUN
cana-1285	45	13	,	,	PUNCT
cana-1285	45	14	compare	compare	VERB
cana-1285	45	15	models	model	NOUN
cana-1285	45	16	with	with	ADP
cana-1285	45	17	minimal	minimal	ADJ
cana-1285	45	18	human	human	ADJ
cana-1285	45	19	participation	participation	NOUN
cana-1285	45	20	.	.	PUNCT
cana-1285	46	1	from	from	ADP
cana-1285	46	2	data	datum	NOUN
cana-1285	46	3	preparation	preparation	NOUN
cana-1285	46	4	to	to	PART
cana-1285	46	5	result	result	VERB
cana-1285	46	6	visualization	visualization	NOUN
cana-1285	46	7	,	,	PUNCT
cana-1285	46	8	this	this	DET
cana-1285	46	9	web	web	NOUN
cana-1285	46	10	-	-	PUNCT
cana-1285	46	11	based	base	VERB
cana-1285	46	12	tool	tool	NOUN
cana-1285	46	13	provides	provide	VERB
cana-1285	46	14	whole	whole	ADJ
cana-1285	46	15	support	support	NOUN
cana-1285	46	16	for	for	ADP
cana-1285	46	17	model	model	NOUN
cana-1285	46	18	development	development	NOUN
cana-1285	46	19	.	.	PUNCT
cana-1285	47	1	deepbio	deepbio	PROPN
cana-1285	47	2	makes	make	VERB
cana-1285	47	3	especially	especially	ADV
cana-1285	47	4	use	use	NOUN
cana-1285	47	5	of	of	ADP
cana-1285	47	6	methods	method	NOUN
cana-1285	47	7	for	for	ADP
cana-1285	47	8	functional	functional	ADJ
cana-1285	47	9	sequential	sequential	ADJ
cana-1285	47	10	region	region	NOUN
cana-1285	47	11	identification	identification	NOUN
cana-1285	47	12	,	,	PUNCT
cana-1285	47	13	model	model	NOUN
cana-1285	47	14	interpretability	interpretability	NOUN
cana-1285	47	15	,	,	PUNCT
cana-1285	47	16	and	and	CCONJ
cana-1285	47	17	feature	feature	NOUN
cana-1285	47	18	analysis	analysis	NOUN
cana-1285	47	19	.	.	PUNCT
cana-1285	48	1	moreover	moreover	ADV
cana-1285	48	2	,	,	PUNCT
cana-1285	48	3	it	it	PRON
cana-1285	48	4	provides	provide	VERB
cana-1285	48	5	graphical	graphical	ADJ
cana-1285	48	6	images	image	NOUN
cana-1285	48	7	to	to	PART
cana-1285	48	8	enhance	enhance	VERB
cana-1285	48	9	the	the	DET
cana-1285	48	10	dependability	dependability	NOUN
cana-1285	48	11	of	of	ADP
cana-1285	48	12	annotated	annotate	VERB
cana-1285	48	13	locations	location	NOUN
cana-1285	48	14	,	,	PUNCT
cana-1285	48	15	therefore	therefore	ADV
cana-1285	48	16	enabling	enable	VERB
cana-1285	48	17	nine	nine	NUM
cana-1285	48	18	basic	basic	ADJ
cana-1285	48	19	-	-	PUNCT
cana-1285	48	20	level	level	NOUN
cana-1285	48	21	functional	functional	ADJ
cana-1285	48	22	annotations	annotation	NOUN
cana-1285	48	23	.	.	PUNCT
cana-1285	49	1	since	since	SCONJ
cana-1285	49	2	researchers	researcher	NOUN
cana-1285	49	3	needing	need	VERB
cana-1285	49	4	to	to	PART
cana-1285	49	5	efficiently	efficiently	ADV
cana-1285	49	6	manage	manage	VERB
cana-1285	49	7	big	big	ADJ
cana-1285	49	8	datasets	dataset	NOUN
cana-1285	49	9	and	and	CCONJ
cana-1285	49	10	hard	hard	ADJ
cana-1285	49	11	biological	biological	ADJ
cana-1285	49	12	challenges	challenge	NOUN
cana-1285	49	13	depend	depend	VERB
cana-1285	49	14	on	on	ADP
cana-1285	49	15	automation	automation	NOUN
cana-1285	49	16	and	and	CCONJ
cana-1285	49	17	interpretability	interpretability	NOUN
cana-1285	49	18	,	,	PUNCT
cana-1285	49	19	deepbio	deepbio	PROPN
cana-1285	49	20	is	be	AUX
cana-1285	49	21	a	a	DET
cana-1285	49	22	great	great	ADJ
cana-1285	49	23	tool	tool	NOUN
cana-1285	49	24	for	for	ADP
cana-1285	49	25	advancing	advance	VERB
cana-1285	49	26	computational	computational	ADJ
cana-1285	49	27	biology	biology	NOUN
cana-1285	49	28	.	.	PUNCT
cana-1285	50	1	combining	combine	VERB
cana-1285	50	2	machine	machine	NOUN
cana-1285	50	3	learning	learning	NOUN
cana-1285	50	4	(	(	PUNCT
cana-1285	50	5	ml	ml	NOUN
cana-1285	50	6	)	)	PUNCT
cana-1285	50	7	with	with	ADP
cana-1285	50	8	deep	deep	ADJ
cana-1285	50	9	learning	learning	NOUN
cana-1285	50	10	(	(	PUNCT
cana-1285	50	11	dl	dl	INTJ
cana-1285	50	12	)	)	PUNCT
cana-1285	50	13	has	have	AUX
cana-1285	50	14	transformed	transform	VERB
cana-1285	50	15	the	the	DET
cana-1285	50	16	biomarker	biomarker	NOUN
cana-1285	50	17	detection	detection	NOUN
cana-1285	50	18	from	from	ADP
cana-1285	50	19	difficult	difficult	ADJ
cana-1285	50	20	multi	multi	ADJ
cana-1285	50	21	-	-	ADJ
cana-1285	50	22	omics	omics	ADJ
cana-1285	50	23	data	datum	NOUN
cana-1285	50	24	in	in	ADP
cana-1285	50	25	healthcare	healthcare	PROPN
cana-1285	50	26	engineering	engineering	NOUN
cana-1285	50	27	.	.	PUNCT
cana-1285	51	1	as	as	SCONJ
cana-1285	51	2	reported	report	VERB
cana-1285	51	3	in	in	ADP
cana-1285	51	4	[	[	X
cana-1285	51	5	13	13	NUM
cana-1285	51	6	]	]	PUNCT
cana-1285	51	7	,	,	PUNCT
cana-1285	51	8	recent	recent	ADJ
cana-1285	51	9	studies	study	NOUN
cana-1285	51	10	on	on	ADP
cana-1285	51	11	numerous	numerous	ADJ
cana-1285	51	12	computational	computational	ADJ
cana-1285	51	13	methods	method	NOUN
cana-1285	51	14	—	—	PUNCT
cana-1285	51	15	including	include	VERB
cana-1285	51	16	feature	feature	NOUN
cana-1285	51	17	selection	selection	NOUN
cana-1285	51	18	strategies	strategy	NOUN
cana-1285	51	19	,	,	PUNCT
cana-1285	51	20	ml	ml	ADP
cana-1285	51	21	,	,	PUNCT
cana-1285	51	22	and	and	CCONJ
cana-1285	51	23	dl	dl	PROPN
cana-1285	51	24	approaches	approach	NOUN
cana-1285	51	25	—	—	PUNCT
cana-1285	51	26	have	have	AUX
cana-1285	51	27	sought	seek	VERB
cana-1285	51	28	markers	marker	NOUN
cana-1285	51	29	in	in	ADP
cana-1285	51	30	single	single	ADJ
cana-1285	51	31	and	and	CCONJ
cana-1285	51	32	multi	multi	ADJ
cana-1285	51	33	-	-	ADJ
cana-1285	51	34	omics	omics	ADJ
cana-1285	51	35	datasets	dataset	NOUN
cana-1285	51	36	.	.	PUNCT
cana-1285	52	1	the	the	DET
cana-1285	52	2	research	research	NOUN
cana-1285	52	3	underlines	underline	VERB
cana-1285	52	4	the	the	DET
cana-1285	52	5	ongoing	ongoing	ADJ
cana-1285	52	6	challenges	challenge	NOUN
cana-1285	52	7	and	and	CCONJ
cana-1285	52	8	constraints	constraint	NOUN
cana-1285	52	9	of	of	ADP
cana-1285	52	10	several	several	ADJ
cana-1285	52	11	approaches	approach	NOUN
cana-1285	52	12	including	include	VERB
cana-1285	52	13	data	datum	NOUN
cana-1285	52	14	dimensionality	dimensionality	NOUN
cana-1285	52	15	,	,	PUNCT
cana-1285	52	16	the	the	DET
cana-1285	52	17	need	need	NOUN
cana-1285	52	18	of	of	ADP
cana-1285	52	19	large	large	ADJ
cana-1285	52	20	labeled	label	VERB
cana-1285	52	21	datasets	dataset	NOUN
cana-1285	52	22	,	,	PUNCT
cana-1285	52	23	and	and	CCONJ
cana-1285	52	24	the	the	DET
cana-1285	52	25	interpretability	interpretability	NOUN
cana-1285	52	26	of	of	ADP
cana-1285	52	27	complex	complex	ADJ
cana-1285	52	28	models	model	NOUN
cana-1285	52	29	.	.	PUNCT
cana-1285	53	1	notwithstanding	notwithstanding	ADP
cana-1285	53	2	these	these	DET
cana-1285	53	3	challenges	challenge	NOUN
cana-1285	53	4	,	,	PUNCT
cana-1285	53	5	ml	ml	ADV
cana-1285	53	6	and	and	CCONJ
cana-1285	53	7	dl	dl	PROPN
cana-1285	53	8	advances	advance	NOUN
cana-1285	53	9	are	be	AUX
cana-1285	53	10	improving	improve	VERB
cana-1285	53	11	the	the	DET
cana-1285	53	12	accuracy	accuracy	NOUN
cana-1285	53	13	and	and	CCONJ
cana-1285	53	14	efficiency	efficiency	NOUN
cana-1285	53	15	of	of	ADP
cana-1285	53	16	biomarketer	biomarketer	NOUN
cana-1285	53	17	discovery	discovery	PROPN
cana-1285	53	18	.	.	PUNCT
cana-1285	54	1	the	the	DET
cana-1285	54	2	need	need	NOUN
cana-1285	54	3	of	of	ADP
cana-1285	54	4	continuous	continuous	ADJ
cana-1285	54	5	invention	invention	NOUN
cana-1285	54	6	and	and	CCONJ
cana-1285	54	7	improvement	improvement	NOUN
cana-1285	54	8	in	in	ADP
cana-1285	54	9	computational	computational	ADJ
cana-1285	54	10	methods	method	NOUN
cana-1285	54	11	is	be	AUX
cana-1285	54	12	therefore	therefore	ADV
cana-1285	54	13	underlined	underline	VERB
cana-1285	54	14	by	by	ADP
cana-1285	54	15	the	the	DET
cana-1285	54	16	study	study	NOUN
cana-1285	54	17	also	also	ADV
cana-1285	54	18	underlining	underline	VERB
cana-1285	54	19	the	the	DET
cana-1285	54	20	necessity	necessity	NOUN
cana-1285	54	21	of	of	ADP
cana-1285	54	22	easily	easily	ADV
cana-1285	54	23	available	available	ADJ
cana-1285	54	24	tools	tool	NOUN
cana-1285	54	25	and	and	CCONJ
cana-1285	54	26	methodologies	methodology	NOUN
cana-1285	54	27	to	to	PART
cana-1285	54	28	enable	enable	VERB
cana-1285	54	29	the	the	DET
cana-1285	54	30	implementation	implementation	NOUN
cana-1285	54	31	of	of	ADP
cana-1285	54	32	these	these	DET
cana-1285	54	33	approaches	approach	NOUN
cana-1285	54	34	in	in	ADP
cana-1285	54	35	biomarker	biomarker	NOUN
cana-1285	54	36	research	research	NOUN
cana-1285	54	37	.	.	PUNCT
cana-1285	55	1	a	a	DET
cana-1285	55	2	significant	significant	ADJ
cana-1285	55	3	development	development	NOUN
cana-1285	55	4	in	in	ADP
cana-1285	55	5	cancer	cancer	NOUN
cana-1285	55	6	diagnosis	diagnosis	NOUN
cana-1285	55	7	is	be	AUX
cana-1285	55	8	the	the	DET
cana-1285	55	9	use	use	NOUN
cana-1285	55	10	of	of	ADP
cana-1285	55	11	deep	deep	ADJ
cana-1285	55	12	learning	learning	NOUN
cana-1285	55	13	algorithms	algorithm	NOUN
cana-1285	55	14	with	with	ADP
cana-1285	55	15	serum	serum	NOUN
cana-1285	55	16	raman	raman	NOUN
cana-1285	55	17	spectroscopy	spectroscopy	NOUN
cana-1285	55	18	.	.	PUNCT
cana-1285	56	1	based	base	VERB
cana-1285	56	2	on	on	ADP
cana-1285	56	3	[	[	X
cana-1285	56	4	14	14	NUM
cana-1285	56	5	]	]	PUNCT
cana-1285	56	6	,	,	PUNCT
cana-1285	56	7	healthy	healthy	ADJ
cana-1285	56	8	controls	control	NOUN
cana-1285	56	9	,	,	PUNCT
cana-1285	56	10	her2	her2	NOUN
cana-1285	56	11	-	-	PUNCT
cana-1285	56	12	positive	positive	ADJ
cana-1285	56	13	breast	breast	NOUN
cana-1285	56	14	cancer	cancer	NOUN
cana-1285	56	15	,	,	PUNCT
cana-1285	56	16	and	and	CCONJ
cana-1285	56	17	triplenegative	triplenegative	VERB
cana-1285	56	18	breast	breast	NOUN
cana-1285	56	19	cancer	cancer	NOUN
cana-1285	56	20	were	be	AUX
cana-1285	56	21	advised	advise	VERB
cana-1285	56	22	a	a	DET
cana-1285	56	23	fast	fast	ADJ
cana-1285	56	24	and	and	CCONJ
cana-1285	56	25	fairly	fairly	ADV
cana-1285	56	26	priced	price	VERB
cana-1285	56	27	diagnostic	diagnostic	ADJ
cana-1285	56	28	technique	technique	NOUN
cana-1285	56	29	.	.	PUNCT
cana-1285	57	1	using	use	VERB
cana-1285	57	2	preprocessed	preprocesse	VERB
cana-1285	57	3	raman	raman	NOUN
cana-1285	57	4	spectra	spectra	NOUN
cana-1285	57	5	as	as	ADP
cana-1285	57	6	deep	deep	ADJ
cana-1285	57	7	learning	learning	NOUN
cana-1285	57	8	model	model	NOUN
cana-1285	57	9	inputs	input	NOUN
cana-1285	57	10	,	,	PUNCT
cana-1285	57	11	the	the	DET
cana-1285	57	12	work	work	NOUN
cana-1285	57	13	comprised	comprise	VERB
cana-1285	57	14	on	on	ADP
cana-1285	57	15	75	75	NUM
cana-1285	57	16	serum	serum	NOUN
cana-1285	57	17	samples	sample	NOUN
cana-1285	57	18	.	.	PUNCT
cana-1285	58	1	we	we	PRON
cana-1285	58	2	investigated	investigate	VERB
cana-1285	58	3	three	three	NUM
cana-1285	58	4	models	model	NOUN
cana-1285	58	5	:	:	PUNCT
cana-1285	58	6	convolutional	convolutional	ADJ
cana-1285	58	7	neural	neural	ADJ
cana-1285	58	8	network	network	NOUN
cana-1285	58	9	,	,	PUNCT
cana-1285	58	10	bidirectional	bidirectional	ADJ
cana-1285	58	11	long	long	ADJ
cana-1285	58	12	-	-	PUNCT
cana-1285	58	13	short	short	ADJ
cana-1285	58	14	-	-	PUNCT
cana-1285	58	15	term	term	NOUN
cana-1285	58	16	memory	memory	NOUN
cana-1285	58	17	network	network	NOUN
cana-1285	58	18	(	(	PUNCT
cana-1285	58	19	bilstm	bilstm	NOUN
cana-1285	58	20	)	)	PUNCT
cana-1285	58	21	,	,	PUNCT
cana-1285	58	22	and	and	CCONJ
cana-1285	58	23	neural	neural	ADJ
cana-1285	58	24	network	network	NOUN
cana-1285	58	25	language	language	NOUN
cana-1285	58	26	model	model	NOUN
cana-1285	58	27	(	(	PUNCT
cana-1285	58	28	nnlm	nnlm	PROPN
cana-1285	58	29	)	)	PUNCT
cana-1285	58	30	.	.	PUNCT
cana-1285	59	1	exceeding	exceed	VERB
cana-1285	59	2	the	the	DET
cana-1285	59	3	nnlm	nnlm	NOUN
cana-1285	59	4	(	(	PUNCT
cana-1285	59	5	87.78	87.78	NUM
cana-1285	59	6	%	%	NOUN
cana-1285	59	7	)	)	PUNCT
cana-1285	59	8	and	and	CCONJ
cana-1285	59	9	bilstm	bilstm	NOUN
cana-1285	59	10	(	(	PUNCT
cana-1285	59	11	90.37	90.37	NUM
cana-1285	59	12	%	%	NOUN
cana-1285	59	13	)	)	PUNCT
cana-1285	59	14	,	,	PUNCT
cana-1285	59	15	the	the	DET
cana-1285	59	16	cnn	cnn	PROPN
cana-1285	59	17	achieved	achieve	VERB
cana-1285	59	18	a	a	DET
cana-1285	59	19	91.11	91.11	NUM
cana-1285	59	20	%	%	NOUN
cana-1285	59	21	accuracy	accuracy	NOUN
cana-1285	59	22	level	level	NOUN
cana-1285	59	23	this	this	DET
cana-1285	59	24	work	work	NOUN
cana-1285	59	25	shows	show	VERB
cana-1285	59	26	the	the	DET
cana-1285	59	27	ability	ability	NOUN
cana-1285	59	28	of	of	ADP
cana-1285	59	29	combining	combine	VERB
cana-1285	59	30	raman	raman	NOUN
cana-1285	59	31	spectroscopy	spectroscopy	NOUN
cana-1285	59	32	with	with	ADP
cana-1285	59	33	deep	deep	ADJ
cana-1285	59	34	learning	learning	NOUN
cana-1285	59	35	to	to	PART
cana-1285	59	36	provide	provide	VERB
cana-1285	59	37	reliable	reliable	ADJ
cana-1285	59	38	diagnosis	diagnosis	NOUN
cana-1285	59	39	tools	tool	NOUN
cana-1285	59	40	for	for	ADP
cana-1285	59	41	breast	breast	NOUN
cana-1285	59	42	cancer	cancer	NOUN
cana-1285	59	43	,	,	PUNCT
cana-1285	59	44	therefore	therefore	ADV
cana-1285	59	45	stressing	stress	VERB
cana-1285	59	46	the	the	DET
cana-1285	59	47	efficacy	efficacy	NOUN
cana-1285	59	48	of	of	ADP
cana-1285	59	49	advanced	advanced	ADJ
cana-1285	59	50	computational	computational	ADJ
cana-1285	59	51	approaches	approach	NOUN
cana-1285	59	52	in	in	ADP
cana-1285	59	53	improving	improve	VERB
cana-1285	59	54	cancer	cancer	NOUN
cana-1285	59	55	diagnosis	diagnosis	NOUN
cana-1285	59	56	.	.	PUNCT
cana-1285	60	1	communications	communication	NOUN
cana-1285	60	2	on	on	ADP
cana-1285	60	3	applied	apply	VERB
cana-1285	60	4	nonlinear	nonlinear	ADJ
cana-1285	60	5	analysis	analysis	NOUN
cana-1285	60	6	issn	issn	NOUN
cana-1285	60	7	:	:	PUNCT
cana-1285	60	8	1074	1074	NUM
cana-1285	60	9	-	-	PUNCT
cana-1285	60	10	133x	133x	NUM
cana-1285	60	11	vol	vol	NOUN
cana-1285	60	12	31	31	NUM
cana-1285	60	13	no	no	NOUN
cana-1285	60	14	.	.	PUNCT
cana-1285	61	1	7s	7	NOUN
cana-1285	61	2	(	(	PUNCT
cana-1285	61	3	2024	2024	NUM
cana-1285	61	4	)	)	PUNCT
cana-1285	61	5	70	70	NUM
cana-1285	61	6	https://internationalpubls.com	https://internationalpubls.com	NUM
cana-1285	61	7	designed	design	VERB
cana-1285	61	8	for	for	ADP
cana-1285	61	9	bioactive	bioactive	ADJ
cana-1285	61	10	peptide	peptide	PROPN
cana-1285	61	11	binary	binary	PROPN
cana-1285	61	12	classification	classification	NOUN
cana-1285	61	13	,	,	PUNCT
cana-1285	61	14	unidl4biopep	unidl4biopep	NOUN
cana-1285	61	15	—	—	PUNCT
cana-1285	61	16	as	as	SCONJ
cana-1285	61	17	described	describe	VERB
cana-1285	61	18	in	in	ADP
cana-1285	61	19	[	[	X
cana-1285	61	20	15	15	NUM
cana-1285	61	21	]	]	PUNCT
cana-1285	61	22	is	be	AUX
cana-1285	61	23	a	a	DET
cana-1285	61	24	universal	universal	ADJ
cana-1285	61	25	deep	deep	ADJ
cana-1285	61	26	-	-	PUNCT
cana-1285	61	27	learning	learn	VERB
cana-1285	61	28	model	model	NOUN
cana-1285	61	29	architecture	architecture	NOUN
cana-1285	61	30	.	.	PUNCT
cana-1285	62	1	by	by	ADP
cana-1285	62	2	use	use	NOUN
cana-1285	62	3	of	of	ADP
cana-1285	62	4	transfer	transfer	NOUN
cana-1285	62	5	learning	learning	NOUN
cana-1285	62	6	,	,	PUNCT
cana-1285	62	7	this	this	DET
cana-1285	62	8	method	method	NOUN
cana-1285	62	9	helps	help	VERB
cana-1285	62	10	users	user	NOUN
cana-1285	62	11	to	to	PART
cana-1285	62	12	construct	construct	VERB
cana-1285	62	13	high	high	ADJ
cana-1285	62	14	-	-	PUNCT
cana-1285	62	15	performance	performance	NOUN
cana-1285	62	16	deep	deep	ADJ
cana-1285	62	17	-	-	PUNCT
cana-1285	62	18	learning	learn	VERB
cana-1285	62	19	models	model	NOUN
cana-1285	62	20	for	for	ADP
cana-1285	62	21	peptide	peptide	ADJ
cana-1285	62	22	discovery	discovery	NOUN
cana-1285	62	23	.	.	PUNCT
cana-1285	63	1	modern	modern	ADJ
cana-1285	63	2	performance	performance	NOUN
cana-1285	63	3	is	be	AUX
cana-1285	63	4	achieved	achieve	VERB
cana-1285	63	5	from	from	ADP
cana-1285	63	6	unidl4biopep	unidl4biopep	PRON
cana-1285	63	7	by	by	ADP
cana-1285	63	8	stable	stable	ADJ
cana-1285	63	9	design	design	NOUN
cana-1285	63	10	demonstrated	demonstrate	VERB
cana-1285	63	11	by	by	ADP
cana-1285	63	12	uniform	uniform	ADJ
cana-1285	63	13	manifold	manifold	ADJ
cana-1285	63	14	approximation	approximation	NOUN
cana-1285	63	15	and	and	CCONJ
cana-1285	63	16	projection	projection	NOUN
cana-1285	63	17	analysis	analysis	NOUN
cana-1285	63	18	.	.	PUNCT
cana-1285	64	1	the	the	DET
cana-1285	64	2	model	model	NOUN
cana-1285	64	3	demonstrated	demonstrate	VERB
cana-1285	64	4	very	very	ADV
cana-1285	64	5	significant	significant	ADJ
cana-1285	64	6	increases	increase	NOUN
cana-1285	64	7	in	in	ADP
cana-1285	64	8	accuracy	accuracy	NOUN
cana-1285	64	9	,	,	PUNCT
cana-1285	64	10	matthews	matthews	PROPN
cana-1285	64	11	correlation	correlation	NOUN
cana-1285	64	12	coefficient	coefficient	NOUN
cana-1285	64	13	,	,	PUNCT
cana-1285	64	14	and	and	CCONJ
cana-1285	64	15	area	area	NOUN
cana-1285	64	16	under	under	ADP
cana-1285	64	17	the	the	DET
cana-1285	64	18	curve	curve	NOUN
cana-1285	64	19	(	(	PUNCT
cana-1285	64	20	auc	auc	NOUN
cana-1285	64	21	)	)	PUNCT
cana-1285	64	22	with	with	ADP
cana-1285	64	23	increments	increment	NOUN
cana-1285	64	24	of	of	ADP
cana-1285	64	25	0.7–7	0.7–7	NOUN
cana-1285	64	26	%	%	NOUN
cana-1285	64	27	,	,	PUNCT
cana-1285	64	28	1.23–26.7	1.23–26.7	NUM
cana-1285	64	29	%	%	NOUN
cana-1285	64	30	,	,	PUNCT
cana-1285	64	31	and	and	CCONJ
cana-1285	64	32	0.3–25.6	0.3–25.6	NUM
cana-1285	64	33	%	%	NOUN
cana-1285	64	34	.	.	PUNCT
cana-1285	65	1	this	this	DET
cana-1285	65	2	progress	progress	NOUN
cana-1285	65	3	highlights	highlight	VERB
cana-1285	65	4	the	the	DET
cana-1285	65	5	ability	ability	NOUN
cana-1285	65	6	of	of	ADP
cana-1285	65	7	the	the	DET
cana-1285	65	8	model	model	NOUN
cana-1285	65	9	to	to	PART
cana-1285	65	10	effectively	effectively	ADV
cana-1285	65	11	control	control	VERB
cana-1285	65	12	bioactive	bioactive	PROPN
cana-1285	65	13	peptide	peptide	NOUN
cana-1285	65	14	data	datum	NOUN
cana-1285	65	15	,	,	PUNCT
cana-1285	65	16	therefore	therefore	ADV
cana-1285	65	17	providing	provide	VERB
cana-1285	65	18	a	a	DET
cana-1285	65	19	useful	useful	ADJ
cana-1285	65	20	tool	tool	NOUN
cana-1285	65	21	for	for	ADP
cana-1285	65	22	peptide	peptide	ADJ
cana-1285	65	23	discovery	discovery	NOUN
cana-1285	65	24	and	and	CCONJ
cana-1285	65	25	categorization	categorization	NOUN
cana-1285	65	26	in	in	ADP
cana-1285	65	27	computational	computational	ADJ
cana-1285	65	28	biology	biology	NOUN
cana-1285	65	29	.	.	PUNCT
cana-1285	66	1	table	table	NOUN
cana-1285	66	2	1	1	NUM
cana-1285	66	3	:	:	PUNCT
cana-1285	66	4	summary	summary	NOUN
cana-1285	66	5	method	method	NOUN
cana-1285	66	6	algorithm	algorithm	NOUN
cana-1285	66	7	methodology	methodology	NOUN
cana-1285	67	1	outcomes	outcome	NOUN
cana-1285	67	2	deepbio	deepbio	ADV
cana-1285	67	3	[	[	X
cana-1285	67	4	12	12	NUM
cana-1285	67	5	]	]	SYM
cana-1285	67	6	42	42	NUM
cana-1285	67	7	deeplearning	deeplearne	VERB
cana-1285	67	8	algorithms	algorithm	NOUN
cana-1285	67	9	automated	automate	VERB
cana-1285	67	10	web	web	NOUN
cana-1285	67	11	service	service	NOUN
cana-1285	67	12	for	for	ADP
cana-1285	67	13	high	high	ADJ
cana-1285	67	14	-	-	PUNCT
cana-1285	67	15	throughput	throughput	NOUN
cana-1285	67	16	biological	biological	ADJ
cana-1285	67	17	sequence	sequence	NOUN
cana-1285	67	18	analysis	analysis	NOUN
cana-1285	67	19	,	,	PUNCT
cana-1285	67	20	including	include	VERB
cana-1285	67	21	model	model	NOUN
cana-1285	67	22	training	training	NOUN
cana-1285	67	23	and	and	CCONJ
cana-1285	67	24	evaluation	evaluation	NOUN
cana-1285	67	25	.	.	PUNCT
cana-1285	68	1	comprehensive	comprehensive	ADJ
cana-1285	68	2	results	result	NOUN
cana-1285	68	3	visualization	visualization	NOUN
cana-1285	68	4	,	,	PUNCT
cana-1285	68	5	high	high	ADJ
cana-1285	68	6	model	model	NOUN
cana-1285	68	7	interpretability	interpretability	NOUN
cana-1285	68	8	,	,	PUNCT
cana-1285	68	9	feature	feature	NOUN
cana-1285	68	10	analysis	analysis	NOUN
cana-1285	68	11	,	,	PUNCT
cana-1285	68	12	and	and	CCONJ
cana-1285	68	13	functional	functional	ADJ
cana-1285	68	14	region	region	NOUN
cana-1285	68	15	discovery	discovery	NOUN
cana-1285	68	16	.	.	PUNCT
cana-1285	69	1	computational	computational	ADJ
cana-1285	69	2	methods	method	NOUN
cana-1285	69	3	[	[	X
cana-1285	69	4	13	13	NUM
cana-1285	69	5	]	]	PUNCT
cana-1285	69	6	various	various	ADJ
cana-1285	69	7	ml	ml	NOUN
cana-1285	69	8	and	and	CCONJ
cana-1285	69	9	dl	dl	PROPN
cana-1285	69	10	techniques	technique	NOUN
cana-1285	69	11	review	review	NOUN
cana-1285	69	12	of	of	ADP
cana-1285	69	13	feature	feature	NOUN
cana-1285	69	14	selection	selection	NOUN
cana-1285	69	15	strategies	strategy	NOUN
cana-1285	69	16	,	,	PUNCT
cana-1285	69	17	ml	ml	X
cana-1285	69	18	and	and	CCONJ
cana-1285	69	19	dl	dl	X
cana-1285	69	20	approaches	approach	NOUN
cana-1285	69	21	for	for	ADP
cana-1285	69	22	biomarker	biomarker	NOUN
cana-1285	69	23	discovery	discovery	NOUN
cana-1285	69	24	in	in	ADP
cana-1285	69	25	multi	multi	ADJ
cana-1285	69	26	-	-	ADJ
cana-1285	69	27	omics	omics	ADJ
cana-1285	69	28	data	datum	NOUN
cana-1285	69	29	.	.	PUNCT
cana-1285	70	1	improved	improved	ADJ
cana-1285	70	2	accuracy	accuracy	NOUN
cana-1285	70	3	in	in	ADP
cana-1285	70	4	biomarker	biomarker	NOUN
cana-1285	70	5	identification	identification	NOUN
cana-1285	70	6	,	,	PUNCT
cana-1285	70	7	ongoing	ongoing	ADJ
cana-1285	70	8	challenges	challenge	NOUN
cana-1285	70	9	in	in	ADP
cana-1285	70	10	data	data	NOUN
cana-1285	70	11	dimensionality	dimensionality	NOUN
cana-1285	70	12	and	and	CCONJ
cana-1285	70	13	interpretability	interpretability	NOUN
cana-1285	70	14	.	.	PUNCT
cana-1285	71	1	breast	breast	NOUN
cana-1285	71	2	cancer	cancer	NOUN
cana-1285	71	3	diagnosis	diagnosis	NOUN
cana-1285	71	4	[	[	X
cana-1285	71	5	14	14	NUM
cana-1285	71	6	]	]	X
cana-1285	71	7	nnlm	nnlm	PROPN
cana-1285	71	8	,	,	PUNCT
cana-1285	71	9	bilstm	bilstm	NOUN
cana-1285	71	10	,	,	PUNCT
cana-1285	71	11	cnn	cnn	PROPN
cana-1285	71	12	utilized	utilize	VERB
cana-1285	71	13	raman	raman	NOUN
cana-1285	71	14	spectroscopy	spectroscopy	NOUN
cana-1285	71	15	data	datum	NOUN
cana-1285	71	16	with	with	ADP
cana-1285	71	17	deep	deep	ADJ
cana-1285	71	18	learning	learning	NOUN
cana-1285	71	19	models	model	NOUN
cana-1285	71	20	for	for	ADP
cana-1285	71	21	cancer	cancer	NOUN
cana-1285	71	22	diagnosis	diagnosis	NOUN
cana-1285	71	23	in	in	ADP
cana-1285	71	24	serum	serum	NOUN
cana-1285	71	25	samples	sample	NOUN
cana-1285	71	26	.	.	PUNCT
cana-1285	72	1	cnn	cnn	PROPN
cana-1285	72	2	achieved	achieve	VERB
cana-1285	72	3	91.11	91.11	NUM
cana-1285	72	4	%	%	NOUN
cana-1285	72	5	accuracy	accuracy	NOUN
cana-1285	72	6	,	,	PUNCT
cana-1285	72	7	demonstrating	demonstrate	VERB
cana-1285	72	8	effective	effective	ADJ
cana-1285	72	9	diagnostic	diagnostic	ADJ
cana-1285	72	10	capability	capability	NOUN
cana-1285	72	11	with	with	ADP
cana-1285	72	12	raman	raman	NOUN
cana-1285	72	13	spectroscopy	spectroscopy	NOUN
cana-1285	72	14	.	.	PUNCT
cana-1285	73	1	unidl4biopep	unidl4biopep	NOUN
cana-1285	74	1	[	[	X
cana-1285	74	2	15	15	NUM
cana-1285	74	3	]	]	X
cana-1285	74	4	fixed	fix	VERB
cana-1285	74	5	deeplearning	deeplearne	VERB
cana-1285	74	6	architecture	architecture	NOUN
cana-1285	74	7	transfer	transfer	NOUN
cana-1285	74	8	learning	learning	NOUN
cana-1285	74	9	model	model	NOUN
cana-1285	74	10	for	for	ADP
cana-1285	74	11	bioactive	bioactive	ADJ
cana-1285	74	12	peptide	peptide	PROPN
cana-1285	74	13	binary	binary	PROPN
cana-1285	74	14	classification	classification	NOUN
cana-1285	74	15	,	,	PUNCT
cana-1285	74	16	validated	validate	VERB
cana-1285	74	17	with	with	ADP
cana-1285	74	18	manifold	manifold	ADJ
cana-1285	74	19	approximation	approximation	NOUN
cana-1285	74	20	analysis	analysis	NOUN
cana-1285	74	21	.	.	PUNCT
cana-1285	75	1	enhanced	enhance	VERB
cana-1285	75	2	accuracy	accuracy	NOUN
cana-1285	75	3	(	(	PUNCT
cana-1285	75	4	up	up	ADP
cana-1285	75	5	to	to	PART
cana-1285	75	6	7	7	NUM
cana-1285	75	7	%	%	NOUN
cana-1285	75	8	)	)	PUNCT
cana-1285	75	9	,	,	PUNCT
cana-1285	75	10	matthews	matthews	PROPN
cana-1285	75	11	correlation	correlation	NOUN
cana-1285	75	12	coefficient	coefficient	NOUN
cana-1285	75	13	(	(	PUNCT
cana-1285	75	14	up	up	ADP
cana-1285	75	15	to	to	PART
cana-1285	75	16	26.7	26.7	NUM
cana-1285	75	17	%	%	NOUN
cana-1285	75	18	)	)	PUNCT
cana-1285	75	19	,	,	PUNCT
cana-1285	75	20	and	and	CCONJ
cana-1285	75	21	auc	auc	NOUN
cana-1285	75	22	(	(	PUNCT
cana-1285	75	23	up	up	ADP
cana-1285	75	24	to	to	PART
cana-1285	75	25	25.6	25.6	NUM
cana-1285	75	26	%	%	NOUN
cana-1285	75	27	)	)	PUNCT
cana-1285	75	28	.	.	PUNCT
cana-1285	76	1	while	while	SCONJ
cana-1285	76	2	current	current	ADJ
cana-1285	76	3	methods	method	NOUN
cana-1285	76	4	such	such	ADJ
cana-1285	76	5	deepbio	deepbio	NOUN
cana-1285	76	6	,	,	PUNCT
cana-1285	76	7	nnlm	nnlm	ADV
cana-1285	76	8	,	,	PUNCT
cana-1285	76	9	and	and	CCONJ
cana-1285	76	10	unidl4biopep	unidl4biopep	NOUN
cana-1285	76	11	show	show	VERB
cana-1285	76	12	great	great	ADJ
cana-1285	76	13	advancement	advancement	NOUN
cana-1285	76	14	in	in	ADP
cana-1285	76	15	their	their	PRON
cana-1285	76	16	respective	respective	ADJ
cana-1285	76	17	sectors	sector	NOUN
cana-1285	76	18	,	,	PUNCT
cana-1285	76	19	nonlinear	nonlinear	ADJ
cana-1285	76	20	dynamics	dynamic	NOUN
cana-1285	76	21	still	still	ADV
cana-1285	76	22	can	can	AUX
cana-1285	76	23	not	not	PART
cana-1285	76	24	be	be	AUX
cana-1285	76	25	effectively	effectively	ADV
cana-1285	76	26	added	add	VERB
cana-1285	76	27	into	into	ADP
cana-1285	76	28	deep	deep	ADJ
cana-1285	76	29	learning	learning	NOUN
cana-1285	76	30	models	model	NOUN
cana-1285	76	31	for	for	ADP
cana-1285	76	32	biochemical	biochemical	ADJ
cana-1285	76	33	component	component	NOUN
cana-1285	76	34	analysis	analysis	NOUN
cana-1285	76	35	.	.	PUNCT
cana-1285	77	1	mostly	mostly	ADV
cana-1285	77	2	aiming	aim	VERB
cana-1285	77	3	at	at	ADP
cana-1285	77	4	interpretability	interpretability	NOUN
cana-1285	77	5	or	or	CCONJ
cana-1285	77	6	forecast	forecast	NOUN
cana-1285	77	7	accuracy	accuracy	NOUN
cana-1285	77	8	,	,	PUNCT
cana-1285	77	9	current	current	ADJ
cana-1285	77	10	methods	method	NOUN
cana-1285	77	11	ignore	ignore	VERB
cana-1285	77	12	the	the	DET
cana-1285	77	13	dynamic	dynamic	ADJ
cana-1285	77	14	constraints	constraint	NOUN
cana-1285	77	15	of	of	ADP
cana-1285	77	16	biological	biological	ADJ
cana-1285	77	17	systems	system	NOUN
cana-1285	77	18	completely	completely	ADV
cana-1285	77	19	.	.	PUNCT
cana-1285	78	1	research	research	NOUN
cana-1285	78	2	has	have	VERB
cana-1285	78	3	to	to	PART
cana-1285	78	4	lead	lead	VERB
cana-1285	78	5	development	development	NOUN
cana-1285	78	6	of	of	ADP
cana-1285	78	7	models	model	NOUN
cana-1285	78	8	that	that	PRON
cana-1285	78	9	not	not	PART
cana-1285	78	10	only	only	ADV
cana-1285	78	11	include	include	VERB
cana-1285	78	12	dynamic	dynamic	ADJ
cana-1285	78	13	system	system	NOUN
cana-1285	78	14	equations	equation	NOUN
cana-1285	78	15	but	but	CCONJ
cana-1285	78	16	also	also	ADV
cana-1285	78	17	maintain	maintain	VERB
cana-1285	78	18	great	great	ADJ
cana-1285	78	19	computational	computational	ADJ
cana-1285	78	20	efficiency	efficiency	NOUN
cana-1285	78	21	and	and	CCONJ
cana-1285	78	22	real	real	ADJ
cana-1285	78	23	-	-	PUNCT
cana-1285	78	24	time	time	NOUN
cana-1285	78	25	applicability	applicability	NOUN
cana-1285	78	26	.	.	PUNCT
cana-1285	79	1	this	this	PRON
cana-1285	79	2	will	will	AUX
cana-1285	79	3	improve	improve	VERB
cana-1285	79	4	overall	overall	ADJ
cana-1285	79	5	analysis	analysis	NOUN
cana-1285	79	6	and	and	CCONJ
cana-1285	79	7	forecast	forecast	NOUN
cana-1285	79	8	accuracy	accuracy	NOUN
cana-1285	79	9	as	as	ADV
cana-1285	79	10	well	well	ADV
cana-1285	79	11	as	as	ADP
cana-1285	79	12	assist	assist	VERB
cana-1285	79	13	to	to	PART
cana-1285	79	14	better	well	ADV
cana-1285	79	15	show	show	VERB
cana-1285	79	16	challenging	challenge	VERB
cana-1285	79	17	biochemical	biochemical	ADJ
cana-1285	79	18	interactions	interaction	NOUN
cana-1285	79	19	.	.	PUNCT
cana-1285	80	1	3	3	X
cana-1285	80	2	.	.	NUM
cana-1285	80	3	proposed	propose	VERB
cana-1285	80	4	method	method	NOUN
cana-1285	80	5	combining	combine	VERB
cana-1285	80	6	statistical	statistical	ADJ
cana-1285	80	7	methods	method	NOUN
cana-1285	80	8	with	with	ADP
cana-1285	80	9	nonlinear	nonlinear	ADJ
cana-1285	80	10	dynamics	dynamic	NOUN
cana-1285	80	11	inside	inside	ADP
cana-1285	80	12	a	a	DET
cana-1285	80	13	deep	deep	ADJ
cana-1285	80	14	learning	learning	NOUN
cana-1285	80	15	framework	framework	NOUN
cana-1285	80	16	helps	help	VERB
cana-1285	80	17	to	to	PART
cana-1285	80	18	improve	improve	VERB
cana-1285	80	19	biochemical	biochemical	ADJ
cana-1285	80	20	component	component	NOUN
cana-1285	80	21	analysis	analysis	NOUN
cana-1285	80	22	.	.	PUNCT
cana-1285	81	1	there	there	PRON
cana-1285	81	2	are	be	VERB
cana-1285	81	3	several	several	ADJ
cana-1285	81	4	crucial	crucial	ADJ
cana-1285	81	5	phases	phase	NOUN
cana-1285	81	6	required	require	VERB
cana-1285	81	7	in	in	ADP
cana-1285	81	8	this	this	DET
cana-1285	81	9	integration	integration	NOUN
cana-1285	81	10	.	.	PUNCT
cana-1285	82	1	communications	communication	NOUN
cana-1285	82	2	on	on	ADP
cana-1285	82	3	applied	apply	VERB
cana-1285	82	4	nonlinear	nonlinear	ADJ
cana-1285	82	5	analysis	analysis	NOUN
cana-1285	82	6	issn	issn	NOUN
cana-1285	82	7	:	:	PUNCT
cana-1285	82	8	1074	1074	NUM
cana-1285	82	9	-	-	PUNCT
cana-1285	82	10	133x	133x	NUM
cana-1285	82	11	vol	vol	NOUN
cana-1285	82	12	31	31	NUM
cana-1285	82	13	no	no	NOUN
cana-1285	82	14	.	.	PUNCT
cana-1285	83	1	7s	7	NOUN
cana-1285	83	2	(	(	PUNCT
cana-1285	83	3	2024	2024	NUM
cana-1285	83	4	)	)	PUNCT
cana-1285	83	5	71	71	NUM
cana-1285	83	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1285	83	7	we	we	PRON
cana-1285	83	8	initially	initially	ADV
cana-1285	83	9	incorporate	incorporate	VERB
cana-1285	83	10	equations	equation	NOUN
cana-1285	83	11	of	of	ADP
cana-1285	83	12	nonlinear	nonlinear	ADJ
cana-1285	83	13	dynamic	dynamic	ADJ
cana-1285	83	14	systems	system	NOUN
cana-1285	83	15	into	into	ADP
cana-1285	83	16	the	the	DET
cana-1285	83	17	deep	deep	ADJ
cana-1285	83	18	neural	neural	ADJ
cana-1285	83	19	network	network	NOUN
cana-1285	83	20	(	(	PUNCT
cana-1285	83	21	dnn	dnn	PROPN
cana-1285	83	22	)	)	PUNCT
cana-1285	83	23	architecture	architecture	NOUN
cana-1285	83	24	.	.	PUNCT
cana-1285	84	1	one	one	NOUN
cana-1285	84	2	achieves	achieve	VERB
cana-1285	84	3	this	this	PRON
cana-1285	84	4	by	by	ADP
cana-1285	84	5	defining	define	VERB
cana-1285	84	6	a	a	DET
cana-1285	84	7	set	set	NOUN
cana-1285	84	8	of	of	ADP
cana-1285	84	9	dynamic	dynamic	ADJ
cana-1285	84	10	equations	equation	NOUN
cana-1285	84	11	reflecting	reflect	VERB
cana-1285	84	12	the	the	DET
cana-1285	84	13	spatial	spatial	ADJ
cana-1285	84	14	and	and	CCONJ
cana-1285	84	15	temporal	temporal	ADJ
cana-1285	84	16	dependencies	dependency	NOUN
cana-1285	84	17	in	in	ADP
cana-1285	84	18	the	the	DET
cana-1285	84	19	biochemical	biochemical	ADJ
cana-1285	84	20	data	datum	NOUN
cana-1285	84	21	.	.	PUNCT
cana-1285	85	1	the	the	DET
cana-1285	85	2	dnn	dnn	PROPN
cana-1285	85	3	then	then	ADV
cana-1285	85	4	carries	carry	VERB
cana-1285	85	5	these	these	DET
cana-1285	85	6	equations	equation	NOUN
cana-1285	85	7	concealed	conceal	VERB
cana-1285	85	8	as	as	ADP
cana-1285	85	9	extra	extra	ADJ
cana-1285	85	10	layers	layer	NOUN
cana-1285	85	11	or	or	CCONJ
cana-1285	85	12	constraints	constraint	NOUN
cana-1285	85	13	.	.	PUNCT
cana-1285	86	1	second	second	ADJ
cana-1285	86	2	,	,	PUNCT
cana-1285	86	3	we	we	PRON
cana-1285	86	4	improve	improve	VERB
cana-1285	86	5	the	the	DET
cana-1285	86	6	model	model	NOUN
cana-1285	86	7	's	's	PART
cana-1285	86	8	forecasts	forecast	NOUN
cana-1285	86	9	by	by	ADP
cana-1285	86	10	means	mean	NOUN
cana-1285	86	11	of	of	ADP
cana-1285	86	12	statistical	statistical	ADJ
cana-1285	86	13	techniques	technique	NOUN
cana-1285	86	14	including	include	VERB
cana-1285	86	15	regression	regression	NOUN
cana-1285	86	16	analysis	analysis	NOUN
cana-1285	86	17	.	.	PUNCT
cana-1285	87	1	this	this	PRON
cana-1285	87	2	is	be	AUX
cana-1285	87	3	matching	match	VERB
cana-1285	87	4	statistical	statistical	ADJ
cana-1285	87	5	models	model	NOUN
cana-1285	87	6	including	include	VERB
cana-1285	87	7	data	datum	NOUN
cana-1285	87	8	variance	variance	NOUN
cana-1285	87	9	and	and	CCONJ
cana-1285	87	10	interaction	interaction	NOUN
cana-1285	87	11	effects	effect	NOUN
cana-1285	87	12	to	to	ADP
cana-1285	87	13	dnn	dnn	PROPN
cana-1285	87	14	outputs	output	NOUN
cana-1285	87	15	.	.	PUNCT
cana-1285	88	1	thirdly	thirdly	ADV
cana-1285	88	2	,	,	PUNCT
cana-1285	88	3	by	by	ADP
cana-1285	88	4	means	mean	NOUN
cana-1285	88	5	of	of	ADP
cana-1285	88	6	variance	variance	NOUN
cana-1285	88	7	decomposition	decomposition	NOUN
cana-1285	88	8	,	,	PUNCT
cana-1285	88	9	we	we	PRON
cana-1285	88	10	assess	assess	VERB
cana-1285	88	11	the	the	DET
cana-1285	88	12	contribution	contribution	NOUN
cana-1285	88	13	of	of	ADP
cana-1285	88	14	different	different	ADJ
cana-1285	88	15	components	component	NOUN
cana-1285	88	16	,	,	PUNCT
cana-1285	88	17	thereby	thereby	ADV
cana-1285	88	18	improving	improve	VERB
cana-1285	88	19	interpretability	interpretability	NOUN
cana-1285	88	20	.	.	PUNCT
cana-1285	89	1	together	together	ADV
cana-1285	89	2	with	with	ADP
cana-1285	89	3	the	the	DET
cana-1285	89	4	normal	normal	ADJ
cana-1285	89	5	prediction	prediction	NOUN
cana-1285	89	6	error	error	NOUN
cana-1285	89	7	,	,	PUNCT
cana-1285	89	8	the	the	DET
cana-1285	89	9	dnn	dnn	PROPN
cana-1285	89	10	is	be	AUX
cana-1285	89	11	trained	train	VERB
cana-1285	89	12	using	use	VERB
cana-1285	89	13	a	a	DET
cana-1285	89	14	combined	combine	VERB
cana-1285	89	15	loss	loss	NOUN
cana-1285	89	16	function	function	NOUN
cana-1285	89	17	comprising	comprise	VERB
cana-1285	89	18	a	a	DET
cana-1285	89	19	regularization	regularization	NOUN
cana-1285	89	20	component	component	NOUN
cana-1285	89	21	obtained	obtain	VERB
cana-1285	89	22	from	from	ADP
cana-1285	89	23	the	the	DET
cana-1285	89	24	dynamic	dynamic	ADJ
cana-1285	89	25	system	system	NOUN
cana-1285	89	26	equations	equation	NOUN
cana-1285	89	27	.	.	PUNCT
cana-1285	90	1	figure	figure	NOUN
cana-1285	90	2	1	1	NUM
cana-1285	90	3	:	:	PUNCT
cana-1285	90	4	proposed	propose	VERB
cana-1285	90	5	workflow	workflow	NOUN
cana-1285	90	6	pseudocode	pseudocode	NOUN
cana-1285	90	7	:	:	PUNCT
cana-1285	90	8	#	#	NOUN
cana-1285	90	9	define	define	VERB
cana-1285	90	10	nonlinear	nonlinear	ADJ
cana-1285	90	11	dynamics	dynamic	NOUN
cana-1285	90	12	def	def	PROPN
cana-1285	90	13	nonlinear_dynamics(input_data	nonlinear_dynamics(input_data	PRON
cana-1285	90	14	):	):	PUNCT
cana-1285	90	15	#	#	NOUN
cana-1285	90	16	define	define	NOUN
cana-1285	90	17	and	and	CCONJ
cana-1285	90	18	return	return	VERB
cana-1285	90	19	nonlinear	nonlinear	ADJ
cana-1285	90	20	dynamic	dynamic	ADJ
cana-1285	90	21	system	system	NOUN
cana-1285	90	22	equations	equation	NOUN
cana-1285	90	23	return	return	VERB
cana-1285	90	24	dynamics_output	dynamics_output	ADJ
cana-1285	90	25	#	#	NOUN
cana-1285	90	26	define	define	VERB
cana-1285	90	27	dnn	dnn	NOUN
cana-1285	90	28	architecture	architecture	NOUN
cana-1285	90	29	with	with	ADP
cana-1285	90	30	dynamics	dynamic	NOUN
cana-1285	90	31	embedding	embed	VERB
cana-1285	90	32	class	class	NOUN
cana-1285	90	33	dynamicdnnmodel(nn.module	dynamicdnnmodel(nn.module	PROPN
cana-1285	90	34	):	):	PUNCT
cana-1285	90	35	def	def	PROPN
cana-1285	90	36	_	_	PUNCT
cana-1285	91	1	_	_	PUNCT
cana-1285	91	2	init__(self	init__(self	PRON
cana-1285	91	3	):	):	PUNCT
cana-1285	91	4	super(dynamicdnnmodel	super(dynamicdnnmodel	PROPN
cana-1285	91	5	,	,	PUNCT
cana-1285	91	6	self).__init	self).__init	AUX
cana-1285	92	1	_	_	NOUN
cana-1285	93	1	_	_	PUNCT
cana-1285	93	2	(	(	PUNCT
cana-1285	93	3	)	)	PUNCT
cana-1285	93	4	self.dnn_layers	self.dnn_layer	NOUN
cana-1285	93	5	=	=	SYM
cana-1285	93	6	nn.sequential	nn.sequential	X
cana-1285	93	7	(	(	PUNCT
cana-1285	93	8	nn.linear(input_dim	nn.linear(input_dim	PROPN
cana-1285	93	9	,	,	PUNCT
cana-1285	93	10	hidden_dim	hidden_dim	PROPN
cana-1285	93	11	)	)	PUNCT
cana-1285	93	12	,	,	PUNCT
cana-1285	93	13	nn.relu	nn.relu	NOUN
cana-1285	93	14	(	(	PUNCT
cana-1285	93	15	)	)	PUNCT
cana-1285	93	16	,	,	PUNCT
cana-1285	93	17	nn.linear(hidden_dim	nn.linear(hidden_dim	PROPN
cana-1285	93	18	,	,	PUNCT
cana-1285	93	19	output_dim	output_dim	NUM
cana-1285	93	20	)	)	PUNCT
cana-1285	93	21	)	)	PUNCT
cana-1285	94	1	communications	communication	NOUN
cana-1285	94	2	on	on	ADP
cana-1285	94	3	applied	apply	VERB
cana-1285	94	4	nonlinear	nonlinear	ADJ
cana-1285	94	5	analysis	analysis	NOUN
cana-1285	94	6	issn	issn	NOUN
cana-1285	94	7	:	:	PUNCT
cana-1285	94	8	1074	1074	NUM
cana-1285	94	9	-	-	PUNCT
cana-1285	94	10	133x	133x	NUM
cana-1285	94	11	vol	vol	NOUN
cana-1285	94	12	31	31	NUM
cana-1285	94	13	no	no	NOUN
cana-1285	94	14	.	.	PUNCT
cana-1285	95	1	7s	7	NOUN
cana-1285	95	2	(	(	PUNCT
cana-1285	95	3	2024	2024	NUM
cana-1285	95	4	)	)	PUNCT
cana-1285	95	5	72	72	NUM
cana-1285	96	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-1285	96	2	def	def	ADJ
cana-1285	96	3	forward(self	forward(self	PROPN
cana-1285	96	4	,	,	PUNCT
cana-1285	96	5	x	x	PRON
cana-1285	96	6	):	):	PUNCT
cana-1285	96	7	dnn_output	dnn_output	NOUN
cana-1285	96	8	=	=	SYM
cana-1285	96	9	self.dnn_layers(x	self.dnn_layers(x	NOUN
cana-1285	96	10	)	)	PUNCT
cana-1285	96	11	dynamics_output	dynamics_output	VERB
cana-1285	96	12	=	=	SYM
cana-1285	96	13	nonlinear_dynamics(x	nonlinear_dynamics(x	NOUN
cana-1285	96	14	)	)	PUNCT
cana-1285	96	15	combined_output	combined_output	NOUN
cana-1285	96	16	=	=	AUX
cana-1285	97	1	dnn_output	dnn_output	PRON
cana-1285	97	2	+	+	CCONJ
cana-1285	97	3	dynamics_output	dynamics_output	VERB
cana-1285	97	4	return	return	NOUN
cana-1285	97	5	combined_output	combined_output	VERB
cana-1285	97	6	#	#	NOUN
cana-1285	97	7	train	train	NOUN
cana-1285	97	8	the	the	DET
cana-1285	97	9	model	model	NOUN
cana-1285	97	10	def	def	PROPN
cana-1285	97	11	train_model(model	train_model(model	PROPN
cana-1285	97	12	,	,	PUNCT
cana-1285	97	13	data_loader	data_loader	PROPN
cana-1285	97	14	,	,	PUNCT
cana-1285	97	15	optimizer	optimizer	NOUN
cana-1285	97	16	,	,	PUNCT
cana-1285	97	17	criterion	criterion	NOUN
cana-1285	97	18	):	):	PUNCT
cana-1285	97	19	model.train	model.train	NOUN
cana-1285	97	20	(	(	PUNCT
cana-1285	97	21	)	)	PUNCT
cana-1285	97	22	for	for	ADP
cana-1285	97	23	batch	batch	NOUN
cana-1285	97	24	in	in	ADP
cana-1285	97	25	data_loader	data_loader	PROPN
cana-1285	97	26	:	:	PUNCT
cana-1285	97	27	inputs	input	NOUN
cana-1285	97	28	,	,	PUNCT
cana-1285	97	29	targets	target	NOUN
cana-1285	97	30	=	=	NOUN
cana-1285	97	31	batch	batch	NOUN
cana-1285	97	32	optimizer.zero_grad	optimizer.zero_grad	ADJ
cana-1285	97	33	(	(	PUNCT
cana-1285	97	34	)	)	PUNCT
cana-1285	97	35	outputs	output	NOUN
cana-1285	97	36	=	=	SYM
cana-1285	97	37	model(inputs	model(inputs	PROPN
cana-1285	97	38	)	)	PUNCT
cana-1285	97	39	loss	loss	NOUN
cana-1285	97	40	=	=	SYM
cana-1285	97	41	criterion(outputs	criterion(output	NOUN
cana-1285	97	42	,	,	PUNCT
cana-1285	97	43	targets	target	NOUN
cana-1285	97	44	)	)	PUNCT
cana-1285	97	45	+	+	NUM
cana-1285	97	46	dynamic_regulation_term(outputs	dynamic_regulation_term(output	NOUN
cana-1285	97	47	)	)	PUNCT
cana-1285	97	48	loss.backward	loss.backward	NOUN
cana-1285	97	49	(	(	PUNCT
cana-1285	97	50	)	)	PUNCT
cana-1285	97	51	optimizer.step	optimizer.step	NOUN
cana-1285	97	52	(	(	PUNCT
cana-1285	97	53	)	)	PUNCT
cana-1285	97	54	#	#	NOUN
cana-1285	97	55	apply	apply	VERB
cana-1285	97	56	statistical	statistical	ADJ
cana-1285	97	57	methods	method	NOUN
cana-1285	97	58	def	def	VERB
cana-1285	97	59	apply_statistical_methods(predictions	apply_statistical_methods(prediction	NOUN
cana-1285	97	60	,	,	PUNCT
cana-1285	97	61	true_values	true_value	NOUN
cana-1285	97	62	):	):	PUNCT
cana-1285	97	63	#	#	NOUN
cana-1285	97	64	fit	fit	ADJ
cana-1285	97	65	regression	regression	NOUN
cana-1285	97	66	models	model	NOUN
cana-1285	97	67	and	and	CCONJ
cana-1285	97	68	compute	compute	NOUN
cana-1285	97	69	variance	variance	NOUN
cana-1285	97	70	decomposition	decomposition	NOUN
cana-1285	97	71	return	return	VERB
cana-1285	97	72	refined_predictions	refined_prediction	NOUN
cana-1285	97	73	#	#	NOUN
cana-1285	97	74	main	main	ADJ
cana-1285	97	75	process	process	NOUN
cana-1285	97	76	model	model	NOUN
cana-1285	97	77	=	=	PROPN
cana-1285	97	78	dynamicdnnmodel	dynamicdnnmodel	PROPN
cana-1285	97	79	(	(	PUNCT
cana-1285	97	80	)	)	PUNCT
cana-1285	97	81	optimizer	optimizer	NOUN
cana-1285	97	82	=	=	SYM
cana-1285	97	83	torch.optim.adam(model.parameters	torch.optim.adam(model.parameter	NOUN
cana-1285	97	84	(	(	PUNCT
cana-1285	97	85	)	)	PUNCT
cana-1285	97	86	)	)	PUNCT
cana-1285	97	87	criterion	criterion	NOUN
cana-1285	97	88	=	=	SYM
cana-1285	97	89	nn.mseloss	nn.mseloss	X
cana-1285	97	90	(	(	PUNCT
cana-1285	97	91	)	)	PUNCT
cana-1285	97	92	data_loader	data_loader	PROPN
cana-1285	97	93	=	=	SYM
cana-1285	97	94	dataloader(dataset	dataloader(dataset	PROPN
cana-1285	97	95	,	,	PUNCT
cana-1285	97	96	batch_size	batch_size	VERB
cana-1285	97	97	=	=	SYM
cana-1285	97	98	batch_size	batch_size	NOUN
cana-1285	97	99	,	,	PUNCT
cana-1285	97	100	shuffle	shuffle	NOUN
cana-1285	97	101	=	=	SYM
cana-1285	97	102	true	true	ADJ
cana-1285	97	103	)	)	PUNCT
cana-1285	97	104	train_model(model	train_model(model	PROPN
cana-1285	97	105	,	,	PUNCT
cana-1285	97	106	data_loader	data_loader	PROPN
cana-1285	97	107	,	,	PUNCT
cana-1285	97	108	optimizer	optimizer	NOUN
cana-1285	97	109	,	,	PUNCT
cana-1285	97	110	criterion	criterion	NOUN
cana-1285	97	111	)	)	PUNCT
cana-1285	97	112	predictions	prediction	NOUN
cana-1285	97	113	=	=	SYM
cana-1285	97	114	model(inputs	model(inputs	PROPN
cana-1285	97	115	)	)	PUNCT
cana-1285	97	116	refined_predictions	refined_prediction	NOUN
cana-1285	97	117	=	=	PUNCT
cana-1285	97	118	apply_statistical_methods(predictions	apply_statistical_methods(prediction	NOUN
cana-1285	97	119	,	,	PUNCT
cana-1285	97	120	true_values	true_value	NOUN
cana-1285	97	121	)	)	PUNCT
cana-1285	97	122	4	4	NUM
cana-1285	97	123	.	.	PUNCT
cana-1285	97	124	proposed	propose	VERB
cana-1285	97	125	nonlinear	nonlinear	ADJ
cana-1285	97	126	dynamics	dynamic	NOUN
cana-1285	97	127	dynamic	dynamic	ADJ
cana-1285	97	128	system	system	NOUN
cana-1285	97	129	equations	equation	NOUN
cana-1285	97	130	included	include	VERB
cana-1285	97	131	into	into	ADP
cana-1285	97	132	the	the	DET
cana-1285	97	133	neural	neural	ADJ
cana-1285	97	134	network	network	NOUN
cana-1285	97	135	architecture	architecture	NOUN
cana-1285	97	136	assist	assist	VERB
cana-1285	97	137	to	to	PART
cana-1285	97	138	capture	capture	VERB
cana-1285	97	139	complex	complex	ADJ
cana-1285	97	140	temporal	temporal	ADJ
cana-1285	97	141	and	and	CCONJ
cana-1285	97	142	spatial	spatial	ADJ
cana-1285	97	143	correlations	correlation	NOUN
cana-1285	97	144	in	in	ADP
cana-1285	97	145	biochemical	biochemical	ADJ
cana-1285	97	146	data	datum	NOUN
cana-1285	97	147	,	,	PUNCT
cana-1285	97	148	thereby	thereby	ADV
cana-1285	97	149	introducing	introduce	VERB
cana-1285	97	150	nonlinear	nonlinear	ADJ
cana-1285	97	151	dynamics	dynamic	NOUN
cana-1285	97	152	into	into	ADP
cana-1285	97	153	the	the	DET
cana-1285	97	154	deep	deep	ADJ
cana-1285	97	155	learning	learning	NOUN
cana-1285	97	156	paradigm	paradigm	NOUN
cana-1285	97	157	.	.	PUNCT
cana-1285	98	1	nonlinear	nonlinear	ADJ
cana-1285	98	2	dynamics	dynamic	NOUN
cana-1285	98	3	describes	describe	VERB
cana-1285	98	4	systems	system	NOUN
cana-1285	98	5	whose	whose	DET
cana-1285	98	6	outputs	output	NOUN
cana-1285	98	7	are	be	AUX
cana-1285	98	8	not	not	PART
cana-1285	98	9	perfectly	perfectly	ADV
cana-1285	98	10	communications	communication	NOUN
cana-1285	98	11	on	on	ADP
cana-1285	98	12	applied	apply	VERB
cana-1285	98	13	nonlinear	nonlinear	ADJ
cana-1285	98	14	analysis	analysis	NOUN
cana-1285	98	15	issn	issn	NOUN
cana-1285	98	16	:	:	PUNCT
cana-1285	98	17	1074	1074	NUM
cana-1285	98	18	-	-	PUNCT
cana-1285	98	19	133x	133x	NUM
cana-1285	98	20	vol	vol	NOUN
cana-1285	98	21	31	31	NUM
cana-1285	98	22	no	no	NOUN
cana-1285	98	23	.	.	PUNCT
cana-1285	99	1	7s	7	NOUN
cana-1285	99	2	(	(	PUNCT
cana-1285	99	3	2024	2024	NUM
cana-1285	99	4	)	)	PUNCT
cana-1285	99	5	73	73	NUM
cana-1285	99	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1285	99	7	proportionate	proportionate	ADJ
cana-1285	99	8	to	to	ADP
cana-1285	99	9	inputs	input	NOUN
cana-1285	99	10	,	,	PUNCT
cana-1285	99	11	therefore	therefore	ADV
cana-1285	99	12	enabling	enable	VERB
cana-1285	99	13	the	the	DET
cana-1285	99	14	modeling	modeling	NOUN
cana-1285	99	15	of	of	ADP
cana-1285	99	16	complicated	complicated	ADJ
cana-1285	99	17	behaviors	behavior	NOUN
cana-1285	99	18	often	often	ADV
cana-1285	99	19	observed	observe	VERB
cana-1285	99	20	in	in	ADP
cana-1285	99	21	biological	biological	ADJ
cana-1285	99	22	processes	process	NOUN
cana-1285	99	23	.	.	PUNCT
cana-1285	100	1	we	we	PRON
cana-1285	100	2	construct	construct	VERB
cana-1285	100	3	the	the	DET
cana-1285	100	4	biochemical	biochemical	ADJ
cana-1285	100	5	system	system	NOUN
cana-1285	100	6	using	use	VERB
cana-1285	100	7	a	a	DET
cana-1285	100	8	collection	collection	NOUN
cana-1285	100	9	of	of	ADP
cana-1285	100	10	nonlinear	nonlinear	ADJ
cana-1285	100	11	differential	differential	ADJ
cana-1285	100	12	equations	equation	NOUN
cana-1285	100	13	in	in	ADP
cana-1285	100	14	this	this	DET
cana-1285	100	15	method	method	NOUN
cana-1285	100	16	.	.	PUNCT
cana-1285	101	1	these	these	DET
cana-1285	101	2	equations	equation	NOUN
cana-1285	101	3	clarify	clarify	VERB
cana-1285	101	4	the	the	DET
cana-1285	101	5	interactions	interaction	NOUN
cana-1285	101	6	among	among	ADP
cana-1285	101	7	numerous	numerous	ADJ
cana-1285	101	8	biological	biological	ADJ
cana-1285	101	9	components	component	NOUN
cana-1285	101	10	and	and	CCONJ
cana-1285	101	11	the	the	DET
cana-1285	101	12	changes	change	NOUN
cana-1285	101	13	in	in	ADP
cana-1285	101	14	the	the	DET
cana-1285	101	15	system	system	NOUN
cana-1285	101	16	condition	condition	NOUN
cana-1285	101	17	with	with	ADP
cana-1285	101	18	time	time	NOUN
cana-1285	101	19	.	.	PUNCT
cana-1285	102	1	general	general	ADJ
cana-1285	102	2	expression	expression	NOUN
cana-1285	102	3	for	for	ADP
cana-1285	102	4	the	the	DET
cana-1285	102	5	dynamic	dynamic	ADJ
cana-1285	102	6	system	system	NOUN
cana-1285	102	7	of	of	ADP
cana-1285	102	8	a	a	DET
cana-1285	102	9	biological	biological	ADJ
cana-1285	102	10	system	system	NOUN
cana-1285	102	11	with	with	ADP
cana-1285	102	12	state	state	NOUN
cana-1285	102	13	variables	variable	NOUN
cana-1285	102	14	x(t	x(t	PROPN
cana-1285	102	15	)	)	PUNCT
cana-1285	102	16	and	and	CCONJ
cana-1285	102	17	inputs	input	VERB
cana-1285	102	18	u(t	u(t	NOUN
cana-1285	102	19	)	)	PUNCT
cana-1285	102	20	can	can	AUX
cana-1285	102	21	be	be	AUX
cana-1285	102	22	nonlinear	nonlinear	ADJ
cana-1285	102	23	differential	differential	ADJ
cana-1285	102	24	equation	equation	NOUN
cana-1285	102	25	:	:	PUNCT
cana-1285	102	26	(	(	PUNCT
cana-1285	102	27	)	)	PUNCT
cana-1285	102	28	(	(	PUNCT
cana-1285	102	29	(	(	PUNCT
cana-1285	102	30	)	)	PUNCT
cana-1285	102	31	,	,	PUNCT
cana-1285	102	32	(	(	PUNCT
cana-1285	102	33	)	)	PUNCT
cana-1285	102	34	,	,	PUNCT
cana-1285	102	35	)	)	PUNCT
cana-1285	103	1	dx	dx	PROPN
cana-1285	104	1	t	t	PROPN
cana-1285	104	2	f	f	PROPN
cana-1285	104	3	x	x	SYM
cana-1285	104	4	t	t	PROPN
cana-1285	104	5	u	u	X
cana-1285	104	6	t	t	X
cana-1285	104	7	dt	dt	PUNCT
cana-1285	105	1	=	=	PROPN
cana-1285	105	2	where	where	SCONJ
cana-1285	105	3	:	:	PUNCT
cana-1285	105	4	x(t	x(t	PROPN
cana-1285	105	5	)	)	PUNCT
cana-1285	105	6	state	state	NOUN
cana-1285	105	7	variables	variable	NOUN
cana-1285	105	8	of	of	ADP
cana-1285	105	9	biochemical	biochemical	ADJ
cana-1285	105	10	system	system	NOUN
cana-1285	105	11	at	at	ADP
cana-1285	105	12	time	time	NOUN
cana-1285	105	13	t	t	PROPN
cana-1285	105	14	,	,	PUNCT
cana-1285	105	15	u(t	u(t	NOUN
cana-1285	105	16	)	)	PUNCT
cana-1285	105	17	external	external	ADJ
cana-1285	105	18	inputs	input	NOUN
cana-1285	105	19	or	or	CCONJ
cana-1285	105	20	control	control	NOUN
cana-1285	105	21	variables	variable	NOUN
cana-1285	105	22	,	,	PUNCT
cana-1285	105	23	θparameters	θparameter	NOUN
cana-1285	105	24	of	of	ADP
cana-1285	105	25	the	the	DET
cana-1285	105	26	system	system	NOUN
cana-1285	105	27	,	,	PUNCT
cana-1285	105	28	f	f	PROPN
cana-1285	105	29	nonlinear	nonlinear	ADJ
cana-1285	105	30	function	function	NOUN
cana-1285	105	31	that	that	PRON
cana-1285	105	32	defines	define	VERB
cana-1285	105	33	the	the	DET
cana-1285	105	34	dynamics	dynamic	NOUN
cana-1285	105	35	of	of	ADP
cana-1285	105	36	the	the	DET
cana-1285	105	37	system	system	NOUN
cana-1285	105	38	.	.	PUNCT
cana-1285	106	1	one	one	PRON
cana-1285	106	2	could	could	AUX
cana-1285	106	3	specify	specify	VERB
cana-1285	106	4	the	the	DET
cana-1285	106	5	function	function	NOUN
cana-1285	106	6	f	f	PROPN
cana-1285	106	7	as	as	ADP
cana-1285	106	8	a	a	DET
cana-1285	106	9	polyn	polyn	NOUN
cana-1285	106	10	,	,	PUNCT
cana-1285	106	11	a	a	DET
cana-1285	106	12	neural	neural	ADJ
cana-1285	106	13	network	network	NOUN
cana-1285	106	14	,	,	PUNCT
cana-1285	106	15	or	or	CCONJ
cana-1285	106	16	any	any	DET
cana-1285	106	17	other	other	ADJ
cana-1285	106	18	nonlinear	nonlinear	ADJ
cana-1285	106	19	function	function	NOUN
cana-1285	106	20	suited	suit	VERB
cana-1285	106	21	for	for	ADP
cana-1285	106	22	the	the	DET
cana-1285	106	23	dynamics	dynamic	NOUN
cana-1285	106	24	of	of	ADP
cana-1285	106	25	the	the	DET
cana-1285	106	26	biological	biological	ADJ
cana-1285	106	27	process	process	NOUN
cana-1285	106	28	.	.	PUNCT
cana-1285	107	1	in	in	ADP
cana-1285	107	2	biological	biological	ADJ
cana-1285	107	3	modeling	modeling	NOUN
cana-1285	107	4	,	,	PUNCT
cana-1285	107	5	one	one	PRON
cana-1285	107	6	often	often	ADV
cana-1285	107	7	used	use	VERB
cana-1285	107	8	nonlinear	nonlinear	ADJ
cana-1285	107	9	function	function	NOUN
cana-1285	107	10	is	be	AUX
cana-1285	107	11	,	,	PUNCT
cana-1285	107	12	for	for	ADP
cana-1285	107	13	example	example	NOUN
cana-1285	107	14	:	:	PUNCT
cana-1285	107	15	2	2	NUM
cana-1285	107	16	1	1	NUM
cana-1285	107	17	2	2	NUM
cana-1285	107	18	3	3	NUM
cana-1285	107	19	(	(	PUNCT
cana-1285	107	20	(	(	PUNCT
cana-1285	107	21	)	)	PUNCT
cana-1285	107	22	,	,	PUNCT
cana-1285	107	23	(	(	PUNCT
cana-1285	107	24	)	)	PUNCT
cana-1285	107	25	,	,	PUNCT
cana-1285	107	26	)	)	PUNCT
cana-1285	108	1	(	(	PUNCT
cana-1285	108	2	)	)	PUNCT
cana-1285	108	3	(	(	PUNCT
cana-1285	108	4	)	)	PUNCT
cana-1285	108	5	(	(	PUNCT
cana-1285	108	6	)	)	PUNCT
cana-1285	108	7	sin	sin	NOUN
cana-1285	108	8	(	(	PUNCT
cana-1285	108	9	(	(	PUNCT
cana-1285	108	10	)	)	PUNCT
cana-1285	108	11	)	)	PUNCT
cana-1285	109	1	f	f	X
cana-1285	109	2	x	x	SYM
cana-1285	109	3	t	t	NOUN
cana-1285	109	4	u	u	X
cana-1285	109	5	t	t	PROPN
cana-1285	109	6	x	x	SYM
cana-1285	109	7	t	t	NOUN
cana-1285	109	8	x	x	SYM
cana-1285	109	9	t	t	NOUN
cana-1285	109	10	u	u	NOUN
cana-1285	109	11	t	t	PROPN
cana-1285	109	12	x	x	SYM
cana-1285	109	13	t	t	NOUN
cana-1285	109	14			PROPN
cana-1285	109	15			X
cana-1285	110	1	=	=	PROPN
cana-1285	110	2	+	+	CCONJ
cana-1285	111	1	+	+	CCONJ
cana-1285	111	2	deep	deep	ADJ
cana-1285	111	3	learning	learning	NOUN
cana-1285	111	4	includes	include	VERB
cana-1285	111	5	these	these	DET
cana-1285	111	6	dynamic	dynamic	ADJ
cana-1285	111	7	equations	equation	NOUN
cana-1285	111	8	into	into	ADP
cana-1285	111	9	the	the	DET
cana-1285	111	10	dnn	dnn	PROPN
cana-1285	111	11	as	as	ADP
cana-1285	111	12	extra	extra	ADJ
cana-1285	111	13	layers	layer	NOUN
cana-1285	111	14	or	or	CCONJ
cana-1285	111	15	constraints	constraint	NOUN
cana-1285	111	16	.	.	PUNCT
cana-1285	112	1	apart	apart	ADV
cana-1285	112	2	from	from	ADP
cana-1285	112	3	expected	expect	VERB
cana-1285	112	4	results	result	NOUN
cana-1285	112	5	from	from	ADP
cana-1285	112	6	the	the	DET
cana-1285	112	7	input	input	NOUN
cana-1285	112	8	data	datum	NOUN
cana-1285	112	9	,	,	PUNCT
cana-1285	112	10	the	the	DET
cana-1285	112	11	network	network	NOUN
cana-1285	112	12	is	be	AUX
cana-1285	112	13	meant	mean	VERB
cana-1285	112	14	to	to	PART
cana-1285	112	15	produce	produce	VERB
cana-1285	112	16	outputs	output	NOUN
cana-1285	112	17	matching	match	VERB
cana-1285	112	18	the	the	DET
cana-1285	112	19	dynamic	dynamic	ADJ
cana-1285	112	20	system	system	NOUN
cana-1285	112	21	equations	equation	NOUN
cana-1285	112	22	.	.	PUNCT
cana-1285	113	1	including	include	VERB
cana-1285	113	2	the	the	DET
cana-1285	113	3	dynamic	dynamic	ADJ
cana-1285	113	4	system	system	NOUN
cana-1285	113	5	equations	equation	NOUN
cana-1285	113	6	into	into	ADP
cana-1285	113	7	the	the	DET
cana-1285	113	8	dnn	dnn	PROPN
cana-1285	113	9	's	's	PART
cana-1285	113	10	loss	loss	NOUN
cana-1285	113	11	function	function	NOUN
cana-1285	113	12	can	can	AUX
cana-1285	113	13	allow	allow	VERB
cana-1285	113	14	one	one	PRON
cana-1285	113	15	to	to	PART
cana-1285	113	16	achieve	achieve	VERB
cana-1285	113	17	this	this	PRON
cana-1285	113	18	:	:	PUNCT
cana-1285	113	19	loss	loss	NOUN
cana-1285	113	20	predictionerror	predictionerror	NOUN
cana-1285	113	21	dynamicregulationterm=	dynamicregulationterm=	PUNCT
cana-1285	114	1	+	+	CCONJ
cana-1285	114	2			NUM
cana-1285	114	3	from	from	ADP
cana-1285	114	4	the	the	DET
cana-1285	114	5	model	model	NOUN
cana-1285	114	6	learning	learn	VERB
cana-1285	114	7	to	to	PART
cana-1285	114	8	account	account	VERB
cana-1285	114	9	for	for	ADP
cana-1285	114	10	complicated	complicated	ADJ
cana-1285	114	11	interactions	interaction	NOUN
cana-1285	114	12	and	and	CCONJ
cana-1285	114	13	temporal	temporal	ADJ
cana-1285	114	14	dependencies	dependency	NOUN
cana-1285	114	15	in	in	ADP
cana-1285	114	16	biochemical	biochemical	ADJ
cana-1285	114	17	data	datum	NOUN
cana-1285	114	18	by	by	ADP
cana-1285	114	19	including	include	VERB
cana-1285	114	20	nonlinear	nonlinear	ADJ
cana-1285	114	21	dynamics	dynamic	NOUN
cana-1285	114	22	into	into	ADP
cana-1285	114	23	the	the	DET
cana-1285	114	24	dnn	dnn	PROPN
cana-1285	114	25	,	,	PUNCT
cana-1285	114	26	improved	improved	ADJ
cana-1285	114	27	accuracy	accuracy	NOUN
cana-1285	114	28	and	and	CCONJ
cana-1285	114	29	interpretability	interpretability	NOUN
cana-1285	114	30	of	of	ADP
cana-1285	114	31	biochemical	biochemical	ADJ
cana-1285	114	32	component	component	NOUN
cana-1285	114	33	analysis	analysis	NOUN
cana-1285	114	34	ensue	ensue	NOUN
cana-1285	114	35	.	.	PUNCT
cana-1285	115	1	5	5	X
cana-1285	115	2	.	.	X
cana-1285	115	3	embed	embed	NOUN
cana-1285	115	4	dynamics	dynamic	NOUN
cana-1285	115	5	into	into	ADP
cana-1285	115	6	dnn	dnn	PROPN
cana-1285	115	7	embedding	embed	VERB
cana-1285	115	8	nonlinear	nonlinear	ADJ
cana-1285	115	9	dynamics	dynamic	NOUN
cana-1285	115	10	into	into	ADP
cana-1285	115	11	a	a	DET
cana-1285	115	12	deep	deep	ADJ
cana-1285	115	13	neural	neural	ADJ
cana-1285	115	14	network	network	NOUN
cana-1285	115	15	(	(	PUNCT
cana-1285	115	16	dnn	dnn	PROPN
cana-1285	115	17	)	)	PUNCT
cana-1285	115	18	means	mean	VERB
cana-1285	115	19	putting	put	VERB
cana-1285	115	20	dynamic	dynamic	ADJ
cana-1285	115	21	system	system	NOUN
cana-1285	115	22	equations	equation	NOUN
cana-1285	115	23	right	right	ADJ
cana-1285	115	24	into	into	ADP
cana-1285	115	25	the	the	DET
cana-1285	115	26	network	network	NOUN
cana-1285	115	27	design	design	NOUN
cana-1285	115	28	thereby	thereby	ADV
cana-1285	115	29	allowing	allow	VERB
cana-1285	115	30	the	the	DET
cana-1285	115	31	model	model	NOUN
cana-1285	115	32	to	to	PART
cana-1285	115	33	integrate	integrate	VERB
cana-1285	115	34	temporal	temporal	ADJ
cana-1285	115	35	and	and	CCONJ
cana-1285	115	36	spatial	spatial	ADJ
cana-1285	115	37	correlations	correlation	NOUN
cana-1285	115	38	inherent	inherent	ADJ
cana-1285	115	39	in	in	ADP
cana-1285	115	40	biological	biological	ADJ
cana-1285	115	41	data	datum	NOUN
cana-1285	115	42	.	.	PUNCT
cana-1285	116	1	this	this	DET
cana-1285	116	2	method	method	NOUN
cana-1285	116	3	improves	improve	VERB
cana-1285	116	4	the	the	DET
cana-1285	116	5	dnn	dnn	PROPN
cana-1285	116	6	's	's	PART
cana-1285	116	7	capacity	capacity	NOUN
cana-1285	116	8	to	to	PART
cana-1285	116	9	capture	capture	VERB
cana-1285	116	10	and	and	CCONJ
cana-1285	116	11	depict	depict	VERB
cana-1285	116	12	complex	complex	ADJ
cana-1285	116	13	interactions	interaction	NOUN
cana-1285	116	14	by	by	ADP
cana-1285	116	15	means	mean	NOUN
cana-1285	116	16	of	of	ADP
cana-1285	116	17	the	the	DET
cana-1285	116	18	structure	structure	NOUN
cana-1285	116	19	and	and	CCONJ
cana-1285	116	20	behavior	behavior	NOUN
cana-1285	116	21	specified	specify	VERB
cana-1285	116	22	by	by	ADP
cana-1285	116	23	nonlinear	nonlinear	ADJ
cana-1285	116	24	dynamics	dynamic	NOUN
cana-1285	116	25	.	.	PUNCT
cana-1285	117	1	we	we	PRON
cana-1285	117	2	develop	develop	VERB
cana-1285	117	3	the	the	DET
cana-1285	117	4	network	network	NOUN
cana-1285	117	5	with	with	ADP
cana-1285	117	6	dynamic	dynamic	ADJ
cana-1285	117	7	components	component	NOUN
cana-1285	117	8	to	to	PART
cana-1285	117	9	integrate	integrate	VERB
cana-1285	117	10	dynamics	dynamic	NOUN
cana-1285	117	11	into	into	ADP
cana-1285	117	12	the	the	DET
cana-1285	117	13	dnn	dnn	PROPN
cana-1285	117	14	.	.	PUNCT
cana-1285	118	1	we	we	PRON
cana-1285	118	2	especially	especially	ADV
cana-1285	118	3	apply	apply	VERB
cana-1285	118	4	additional	additional	ADJ
cana-1285	118	5	layers	layer	NOUN
cana-1285	118	6	or	or	CCONJ
cana-1285	118	7	modifications	modification	NOUN
cana-1285	118	8	to	to	PART
cana-1285	118	9	enforce	enforce	VERB
cana-1285	118	10	the	the	DET
cana-1285	118	11	restrictions	restriction	NOUN
cana-1285	118	12	of	of	ADP
cana-1285	118	13	the	the	DET
cana-1285	118	14	nonlinear	nonlinear	ADJ
cana-1285	118	15	dynamic	dynamic	ADJ
cana-1285	118	16	system	system	NOUN
cana-1285	118	17	.	.	PUNCT
cana-1285	119	1	let	let	VERB
cana-1285	119	2	the	the	DET
cana-1285	119	3	first	first	ADJ
cana-1285	119	4	dnn	dnn	PROPN
cana-1285	119	5	model	model	NOUN
cana-1285	119	6	project	project	NOUN
cana-1285	119	7	from	from	ADP
cana-1285	119	8	inputs	input	NOUN
cana-1285	119	9	.	.	PUNCT
cana-1285	120	1	the	the	DET
cana-1285	120	2	integration	integration	NOUN
cana-1285	120	3	consists	consist	VERB
cana-1285	120	4	in	in	ADP
cana-1285	120	5	building	build	VERB
cana-1285	120	6	a	a	DET
cana-1285	120	7	dynamic	dynamic	ADJ
cana-1285	120	8	module	module	NOUN
cana-1285	120	9	containing	contain	VERB
cana-1285	120	10	the	the	DET
cana-1285	120	11	differential	differential	ADJ
cana-1285	120	12	equations	equation	NOUN
cana-1285	120	13	of	of	ADP
cana-1285	120	14	the	the	DET
cana-1285	120	15	system	system	NOUN
cana-1285	120	16	into	into	ADP
cana-1285	120	17	the	the	DET
cana-1285	120	18	prediction	prediction	NOUN
cana-1285	120	19	mechanism	mechanism	NOUN
cana-1285	120	20	.	.	PUNCT
cana-1285	121	1	consider	consider	VERB
cana-1285	121	2	a	a	DET
cana-1285	121	3	nonlinear	nonlinear	ADJ
cana-1285	121	4	dynamic	dynamic	ADJ
cana-1285	121	5	system	system	NOUN
cana-1285	121	6	stated	state	VERB
cana-1285	121	7	by	by	ADP
cana-1285	121	8	:	:	PUNCT
cana-1285	121	9	communications	communication	NOUN
cana-1285	121	10	on	on	ADP
cana-1285	121	11	applied	apply	VERB
cana-1285	121	12	nonlinear	nonlinear	ADJ
cana-1285	121	13	analysis	analysis	NOUN
cana-1285	121	14	issn	issn	NOUN
cana-1285	121	15	:	:	PUNCT
cana-1285	121	16	1074	1074	NUM
cana-1285	121	17	-	-	PUNCT
cana-1285	121	18	133x	133x	NUM
cana-1285	121	19	vol	vol	NOUN
cana-1285	121	20	31	31	NUM
cana-1285	121	21	no	no	NOUN
cana-1285	121	22	.	.	PUNCT
cana-1285	122	1	7s	7	NOUN
cana-1285	122	2	(	(	PUNCT
cana-1285	122	3	2024	2024	NUM
cana-1285	122	4	)	)	PUNCT
cana-1285	122	5	74	74	NUM
cana-1285	122	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1285	122	7	(	(	PUNCT
cana-1285	122	8	)	)	PUNCT
cana-1285	122	9	(	(	PUNCT
cana-1285	122	10	(	(	PUNCT
cana-1285	122	11	)	)	PUNCT
cana-1285	122	12	,	,	PUNCT
cana-1285	122	13	(	(	PUNCT
cana-1285	122	14	)	)	PUNCT
cana-1285	122	15	,	,	PUNCT
cana-1285	122	16	)	)	PUNCT
cana-1285	122	17	dx	dx	PROPN
cana-1285	123	1	t	t	PROPN
cana-1285	123	2	f	f	PROPN
cana-1285	123	3	x	x	SYM
cana-1285	123	4	t	t	PROPN
cana-1285	123	5	u	u	X
cana-1285	123	6	t	t	X
cana-1285	123	7	dt	dt	PUNCT
cana-1285	124	1	=	=	PROPN
cana-1285	124	2	where	where	SCONJ
cana-1285	124	3	f	f	PROPN
cana-1285	124	4	nonlinear	nonlinear	ADJ
cana-1285	124	5	function	function	NOUN
cana-1285	124	6	representing	represent	VERB
cana-1285	124	7	the	the	DET
cana-1285	124	8	system	system	NOUN
cana-1285	124	9	dynamics	dynamic	NOUN
cana-1285	124	10	.	.	PUNCT
cana-1285	125	1	we	we	PRON
cana-1285	125	2	augment	augment	VERB
cana-1285	125	3	the	the	DET
cana-1285	125	4	dnn	dnn	PROPN
cana-1285	125	5	including	include	VERB
cana-1285	125	6	a	a	DET
cana-1285	125	7	dynamic	dynamic	ADJ
cana-1285	125	8	module	module	NOUN
cana-1285	125	9	.	.	PUNCT
cana-1285	126	1	one	one	PRON
cana-1285	126	2	may	may	AUX
cana-1285	126	3	describe	describe	VERB
cana-1285	126	4	the	the	DET
cana-1285	126	5	better	well	ADJ
cana-1285	126	6	model	model	NOUN
cana-1285	126	7	output	output	NOUN
cana-1285	126	8	as	as	ADP
cana-1285	126	9	:	:	PUNCT
cana-1285	126	10	ˆ	ˆ	PRON
cana-1285	126	11	dnn	dnn	PROPN
cana-1285	126	12	(	(	PUNCT
cana-1285	126	13	)	)	PUNCT
cana-1285	126	14	dynamics	dynamic	NOUN
cana-1285	126	15	(	(	PUNCT
cana-1285	126	16	)	)	PUNCT
cana-1285	126	17	y	y	PROPN
cana-1285	126	18	x	x	SYM
cana-1285	126	19	x=	x=	PUNCT
cana-1285	127	1	+	+	CCONJ
cana-1285	127	2	where	where	SCONJ
cana-1285	127	3	,	,	PUNCT
cana-1285	127	4	dynamics(x	dynamics(x	NOUN
cana-1285	127	5	)	)	PUNCT
cana-1285	127	6	included	include	VERB
cana-1285	127	7	as	as	ADP
cana-1285	127	8	a	a	DET
cana-1285	127	9	layer	layer	NOUN
cana-1285	127	10	above	above	ADP
cana-1285	127	11	the	the	DET
cana-1285	127	12	network	network	NOUN
cana-1285	127	13	is	be	AUX
cana-1285	127	14	a	a	DET
cana-1285	127	15	function	function	NOUN
cana-1285	127	16	solves	solve	VERB
cana-1285	127	17	the	the	DET
cana-1285	127	18	nonlinear	nonlinear	ADJ
cana-1285	127	19	differential	differential	ADJ
cana-1285	127	20	equation	equation	NOUN
cana-1285	127	21	.	.	PUNCT
cana-1285	128	1	one	one	PRON
cana-1285	128	2	can	can	AUX
cana-1285	128	3	capture	capture	VERB
cana-1285	128	4	temporal	temporal	ADJ
cana-1285	128	5	dynamics	dynamic	NOUN
cana-1285	128	6	either	either	CCONJ
cana-1285	128	7	explicitly	explicitly	ADV
cana-1285	128	8	including	include	VERB
cana-1285	128	9	dynamic	dynamic	ADJ
cana-1285	128	10	functions	function	NOUN
cana-1285	128	11	into	into	ADP
cana-1285	128	12	the	the	DET
cana-1285	128	13	feedforward	feedforward	ADJ
cana-1285	128	14	structure	structure	NOUN
cana-1285	128	15	or	or	CCONJ
cana-1285	128	16	recurrent	recurrent	ADJ
cana-1285	128	17	layers	layer	NOUN
cana-1285	128	18	—	—	PUNCT
cana-1285	128	19	e.g.	e.g.	ADV
cana-1285	128	20	,	,	PUNCT
cana-1285	128	21	lstm	lstm	NOUN
cana-1285	128	22	or	or	CCONJ
cana-1285	128	23	gru	gru	PROPN
cana-1285	128	24	.	.	PROPN
cana-1285	129	1	6	6	NUM
cana-1285	129	2	.	.	X
cana-1285	129	3	dynamic	dynamic	ADJ
cana-1285	129	4	system	system	NOUN
cana-1285	129	5	constraints	constraint	NOUN
cana-1285	129	6	in	in	ADP
cana-1285	129	7	loss	loss	NOUN
cana-1285	129	8	function	function	NOUN
cana-1285	129	9	the	the	DET
cana-1285	129	10	network	network	NOUN
cana-1285	129	11	's	's	PART
cana-1285	129	12	training	training	NOUN
cana-1285	129	13	process	process	NOUN
cana-1285	129	14	has	have	VERB
cana-1285	129	15	to	to	PART
cana-1285	129	16	follow	follow	VERB
cana-1285	129	17	dynamic	dynamic	ADJ
cana-1285	129	18	equation	equation	NOUN
cana-1285	129	19	adherance	adherance	NOUN
cana-1285	129	20	.	.	PUNCT
cana-1285	130	1	we	we	PRON
cana-1285	130	2	do	do	VERB
cana-1285	130	3	this	this	PRON
cana-1285	130	4	by	by	ADP
cana-1285	130	5	considering	consider	VERB
cana-1285	130	6	a	a	DET
cana-1285	130	7	factor	factor	NOUN
cana-1285	130	8	in	in	ADP
cana-1285	130	9	the	the	DET
cana-1285	130	10	loss	loss	NOUN
cana-1285	130	11	function	function	NOUN
cana-1285	130	12	punishing	punish	VERB
cana-1285	130	13	deviations	deviation	NOUN
cana-1285	130	14	from	from	ADP
cana-1285	130	15	the	the	DET
cana-1285	130	16	dynamic	dynamic	ADJ
cana-1285	130	17	system	system	NOUN
cana-1285	130	18	limitations	limitation	NOUN
cana-1285	130	19	.	.	PUNCT
cana-1285	131	1	the	the	DET
cana-1285	131	2	general	general	ADJ
cana-1285	131	3	loss	loss	NOUN
cana-1285	131	4	function	function	NOUN
cana-1285	131	5	of	of	ADP
cana-1285	131	6	the	the	DET
cana-1285	131	7	dnn	dnn	PROPN
cana-1285	131	8	is	be	AUX
cana-1285	131	9	stated	state	VERB
cana-1285	131	10	as	as	SCONJ
cana-1285	131	11	follows	follow	VERB
cana-1285	131	12	:	:	PUNCT
cana-1285	131	13	prediction	prediction	NOUN
cana-1285	131	14	dynamicsloss	dynamicsloss	ADJ
cana-1285	131	15	loss=	loss=	PROPN
cana-1285	132	1	+	+	NUM
cana-1285	132	2	l	l	NOUN
cana-1285	132	3	2	2	NUM
cana-1285	132	4	prediction	prediction	NOUN
cana-1285	132	5	1	1	NUM
cana-1285	132	6	1	1	NUM
cana-1285	132	7	ˆloss	ˆloss	ADV
cana-1285	132	8	(	(	PUNCT
cana-1285	132	9	)	)	PUNCT
cana-1285	132	10	n	n	CCONJ
cana-1285	133	1	i	i	PRON
cana-1285	133	2	i	i	PRON
cana-1285	134	1	i	i	VERB
cana-1285	134	2	y	y	VERB
cana-1285	134	3	y	y	PROPN
cana-1285	134	4	n	n	NOUN
cana-1285	134	5	=	=	SYM
cana-1285	135	1	=	=	NOUN
cana-1285	135	2	−	−	NOUN
cana-1285	135	3	2	2	NUM
cana-1285	135	4	dynamics	dynamic	NOUN
cana-1285	135	5	1	1	NUM
cana-1285	135	6	(	(	PUNCT
cana-1285	135	7	)	)	SYM
cana-1285	135	8	1	1	NUM
cana-1285	135	9	loss	loss	NOUN
cana-1285	135	10	(	(	PUNCT
cana-1285	135	11	(	(	PUNCT
cana-1285	135	12	)	)	PUNCT
cana-1285	135	13	,	,	PUNCT
cana-1285	135	14	(	(	PUNCT
cana-1285	135	15	)	)	PUNCT
cana-1285	135	16	,	,	PUNCT
cana-1285	135	17	)	)	PUNCT
cana-1285	135	18	n	n	CCONJ
cana-1285	136	1	i	i	PRON
cana-1285	136	2	i	i	PRON
cana-1285	137	1	i	i	PRON
cana-1285	137	2	i	i	PRON
cana-1285	138	1	dx	dx	PROPN
cana-1285	139	1	t	t	PROPN
cana-1285	139	2	f	f	PROPN
cana-1285	139	3	x	x	SYM
cana-1285	139	4	t	t	PROPN
cana-1285	139	5	u	u	NOUN
cana-1285	139	6	t	t	PROPN
cana-1285	139	7	n	n	ADV
cana-1285	139	8	dt	dt	NOUN
cana-1285	139	9			X
cana-1285	139	10	=	=	PUNCT
cana-1285	139	11	=	=	PUNCT
cana-1285	140	1	−	−	NOUN
cana-1285	140	2	where	where	SCONJ
cana-1285	140	3	λ	λ	PROPN
cana-1285	140	4	strikes	strike	VERB
cana-1285	140	5	a	a	DET
cana-1285	140	6	compromise	compromise	NOUN
cana-1285	140	7	between	between	ADP
cana-1285	140	8	forecast	forecast	NOUN
cana-1285	140	9	accuracy	accuracy	NOUN
cana-1285	140	10	and	and	CCONJ
cana-1285	140	11	adherence	adherence	NOUN
cana-1285	140	12	to	to	ADP
cana-1285	140	13	dynamic	dynamic	ADJ
cana-1285	140	14	restrictions	restriction	NOUN
cana-1285	140	15	.	.	PUNCT
cana-1285	141	1	comprising	comprise	VERB
cana-1285	141	2	dynamic	dynamic	ADJ
cana-1285	141	3	system	system	NOUN
cana-1285	141	4	constraints	constraint	NOUN
cana-1285	141	5	as	as	ADV
cana-1285	141	6	well	well	ADV
cana-1285	141	7	as	as	ADP
cana-1285	141	8	the	the	DET
cana-1285	141	9	prediction	prediction	NOUN
cana-1285	141	10	error	error	NOUN
cana-1285	141	11	,	,	PUNCT
cana-1285	141	12	the	the	DET
cana-1285	141	13	dnn	dnn	PROPN
cana-1285	141	14	is	be	AUX
cana-1285	141	15	designed	design	VERB
cana-1285	141	16	to	to	PART
cana-1285	141	17	minimize	minimize	VERB
cana-1285	141	18	the	the	DET
cana-1285	141	19	overall	overall	ADJ
cana-1285	141	20	loss	loss	NOUN
cana-1285	141	21	during	during	ADP
cana-1285	141	22	training	training	NOUN
cana-1285	141	23	.	.	PUNCT
cana-1285	142	1	this	this	DET
cana-1285	142	2	approach	approach	NOUN
cana-1285	142	3	ensures	ensure	VERB
cana-1285	142	4	both	both	DET
cana-1285	142	5	precise	precise	ADJ
cana-1285	142	6	predictions	prediction	NOUN
cana-1285	142	7	and	and	CCONJ
cana-1285	142	8	respect	respect	NOUN
cana-1285	142	9	of	of	ADP
cana-1285	142	10	the	the	DET
cana-1285	142	11	basic	basic	ADJ
cana-1285	142	12	dynamic	dynamic	ADJ
cana-1285	142	13	behavior	behavior	NOUN
cana-1285	142	14	of	of	ADP
cana-1285	142	15	the	the	DET
cana-1285	142	16	biological	biological	ADJ
cana-1285	142	17	system	system	NOUN
cana-1285	142	18	.	.	PUNCT
cana-1285	143	1	function	function	PROPN
cana-1285	143	2	dynamicsystemconstrainedlossfunction(model	dynamicsystemconstrainedlossfunction(model	PROPN
cana-1285	143	3	,	,	PUNCT
cana-1285	143	4	data	datum	NOUN
cana-1285	143	5	,	,	PUNCT
cana-1285	143	6	constraints	constraint	NOUN
cana-1285	143	7	,	,	PUNCT
cana-1285	143	8	lambda	lambda	NOUN
cana-1285	143	9	):	):	PUNCT
cana-1285	143	10	input	input	NOUN
cana-1285	143	11	:	:	PUNCT
cana-1285	143	12	model	model	NOUN
cana-1285	143	13	:	:	PUNCT
cana-1285	143	14	the	the	DET
cana-1285	143	15	machine	machine	NOUN
cana-1285	143	16	learning	learn	VERB
cana-1285	143	17	model	model	NOUN
cana-1285	143	18	being	be	AUX
cana-1285	143	19	trained	train	VERB
cana-1285	143	20	data	datum	NOUN
cana-1285	143	21	:	:	PUNCT
cana-1285	143	22	dataset	dataset	NOUN
cana-1285	143	23	consisting	consist	VERB
cana-1285	143	24	of	of	ADP
cana-1285	143	25	input	input	NOUN
cana-1285	143	26	features	feature	NOUN
cana-1285	143	27	and	and	CCONJ
cana-1285	143	28	corresponding	correspond	VERB
cana-1285	143	29	target	target	NOUN
cana-1285	143	30	values	value	NOUN
cana-1285	143	31	constraints	constraint	NOUN
cana-1285	143	32	:	:	PUNCT
cana-1285	143	33	list	list	NOUN
cana-1285	143	34	of	of	ADP
cana-1285	143	35	dynamic	dynamic	ADJ
cana-1285	143	36	constraints	constraint	NOUN
cana-1285	143	37	functions	function	NOUN
cana-1285	143	38	lambda	lambda	NOUN
cana-1285	143	39	:	:	PUNCT
cana-1285	143	40	penalty	penalty	NOUN
cana-1285	143	41	parameter	parameter	NOUN
cana-1285	143	42	for	for	ADP
cana-1285	143	43	constraints	constraint	NOUN
cana-1285	143	44	output	output	NOUN
cana-1285	143	45	:	:	PUNCT
cana-1285	143	46	total_loss	total_loss	NUM
cana-1285	143	47	:	:	PUNCT
cana-1285	143	48	the	the	DET
cana-1285	143	49	loss	loss	NOUN
cana-1285	143	50	value	value	NOUN
cana-1285	143	51	incorporating	incorporate	VERB
cana-1285	143	52	the	the	DET
cana-1285	143	53	constraints	constraint	NOUN
cana-1285	143	54	#	#	NOUN
cana-1285	143	55	initialize	initialize	NOUN
cana-1285	143	56	total	total	ADJ
cana-1285	143	57	loss	loss	NOUN
cana-1285	143	58	total_loss	total_loss	NOUN
cana-1285	143	59	=	=	SYM
cana-1285	143	60	0.0	0.0	NUM
cana-1285	143	61	#	#	NOUN
cana-1285	143	62	compute	compute	NOUN
cana-1285	143	63	the	the	DET
cana-1285	143	64	primary	primary	ADJ
cana-1285	143	65	loss	loss	NOUN
cana-1285	143	66	(	(	PUNCT
cana-1285	143	67	e.g.	e.g.	ADV
cana-1285	143	68	,	,	PUNCT
cana-1285	143	69	mean	mean	VERB
cana-1285	143	70	squared	square	VERB
cana-1285	143	71	error	error	NOUN
cana-1285	143	72	,	,	PUNCT
cana-1285	143	73	cross	cross	NOUN
cana-1285	143	74	-	-	ADJ
cana-1285	143	75	entropy	entropy	ADJ
cana-1285	143	76	)	)	PUNCT
cana-1285	143	77	communications	communication	NOUN
cana-1285	143	78	on	on	ADP
cana-1285	143	79	applied	apply	VERB
cana-1285	143	80	nonlinear	nonlinear	ADJ
cana-1285	143	81	analysis	analysis	NOUN
cana-1285	143	82	issn	issn	NOUN
cana-1285	143	83	:	:	PUNCT
cana-1285	143	84	1074	1074	NUM
cana-1285	143	85	-	-	PUNCT
cana-1285	143	86	133x	133x	NUM
cana-1285	143	87	vol	vol	NOUN
cana-1285	143	88	31	31	NUM
cana-1285	143	89	no	no	NOUN
cana-1285	143	90	.	.	PUNCT
cana-1285	144	1	7s	7	NOUN
cana-1285	144	2	(	(	PUNCT
cana-1285	144	3	2024	2024	NUM
cana-1285	144	4	)	)	PUNCT
cana-1285	144	5	75	75	NUM
cana-1285	144	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1285	144	7	primary_loss	primary_loss	X
cana-1285	144	8	=	=	SYM
cana-1285	144	9	computeprimaryloss(model	computeprimaryloss(model	PROPN
cana-1285	144	10	,	,	PUNCT
cana-1285	144	11	data	datum	NOUN
cana-1285	144	12	)	)	PUNCT
cana-1285	144	13	#	#	NOUN
cana-1285	144	14	initialize	initialize	NOUN
cana-1285	144	15	constraints	constraint	NOUN
cana-1285	144	16	penalty	penalty	NOUN
cana-1285	144	17	constraints_penalty	constraints_penalty	NOUN
cana-1285	144	18	=	=	SYM
cana-1285	144	19	0.0	0.0	NUM
cana-1285	144	20	#	#	NOUN
cana-1285	144	21	iterate	iterate	NOUN
cana-1285	144	22	over	over	ADP
cana-1285	144	23	each	each	DET
cana-1285	144	24	constraint	constraint	NOUN
cana-1285	144	25	function	function	NOUN
cana-1285	144	26	for	for	ADP
cana-1285	144	27	each	each	DET
cana-1285	144	28	constraint	constraint	NOUN
cana-1285	144	29	in	in	ADP
cana-1285	144	30	constraints	constraint	NOUN
cana-1285	144	31	:	:	PUNCT
cana-1285	144	32	#	#	NOUN
cana-1285	144	33	compute	compute	NOUN
cana-1285	144	34	the	the	DET
cana-1285	144	35	constraint	constraint	NOUN
cana-1285	144	36	violation	violation	NOUN
cana-1285	144	37	for	for	ADP
cana-1285	144	38	the	the	DET
cana-1285	144	39	current	current	ADJ
cana-1285	144	40	constraint	constraint	NOUN
cana-1285	144	41	function	function	NOUN
cana-1285	144	42	violation	violation	NOUN
cana-1285	144	43	=	=	SYM
cana-1285	144	44	constraint(model	constraint(model	PROPN
cana-1285	144	45	,	,	PUNCT
cana-1285	144	46	data	datum	NOUN
cana-1285	144	47	)	)	PUNCT
cana-1285	144	48	#	#	NOUN
cana-1285	144	49	add	add	VERB
cana-1285	144	50	the	the	DET
cana-1285	144	51	penalty	penalty	NOUN
cana-1285	144	52	for	for	ADP
cana-1285	144	53	the	the	DET
cana-1285	144	54	constraint	constraint	NOUN
cana-1285	144	55	violation	violation	NOUN
cana-1285	144	56	constraints_penalty	constraints_penalty	NOUN
cana-1285	145	1	+	+	NOUN
cana-1285	145	2	=	=	SYM
cana-1285	145	3	violation	violation	NOUN
cana-1285	145	4	#	#	NOUN
cana-1285	145	5	incorporate	incorporate	VERB
cana-1285	145	6	constraints	constraint	NOUN
cana-1285	145	7	penalty	penalty	NOUN
cana-1285	145	8	into	into	ADP
cana-1285	145	9	the	the	DET
cana-1285	145	10	total	total	ADJ
cana-1285	145	11	loss	loss	NOUN
cana-1285	145	12	total_loss	total_los	NOUN
cana-1285	145	13	=	=	PUNCT
cana-1285	145	14	primary_loss	primary_loss	X
cana-1285	145	15	+	+	CCONJ
cana-1285	146	1	lambda	lambda	ADJ
cana-1285	146	2	*	*	PUNCT
cana-1285	146	3	constraints_penalty	constraints_penalty	NOUN
cana-1285	146	4	return	return	VERB
cana-1285	146	5	total_loss	total_loss	X
cana-1285	146	6	function	function	NOUN
cana-1285	146	7	computeprimaryloss(model	computeprimaryloss(model	PROPN
cana-1285	146	8	,	,	PUNCT
cana-1285	146	9	data	datum	NOUN
cana-1285	146	10	):	):	PUNCT
cana-1285	146	11	input	input	NOUN
cana-1285	146	12	:	:	PUNCT
cana-1285	146	13	model	model	NOUN
cana-1285	146	14	:	:	PUNCT
cana-1285	146	15	the	the	DET
cana-1285	146	16	machine	machine	NOUN
cana-1285	146	17	learning	learn	VERB
cana-1285	146	18	model	model	NOUN
cana-1285	146	19	being	be	AUX
cana-1285	146	20	trained	train	VERB
cana-1285	146	21	data	datum	NOUN
cana-1285	146	22	:	:	PUNCT
cana-1285	146	23	dataset	dataset	NOUN
cana-1285	146	24	consisting	consist	VERB
cana-1285	146	25	of	of	ADP
cana-1285	146	26	input	input	NOUN
cana-1285	146	27	features	feature	NOUN
cana-1285	146	28	and	and	CCONJ
cana-1285	146	29	corresponding	correspond	VERB
cana-1285	146	30	target	target	NOUN
cana-1285	146	31	values	value	NOUN
cana-1285	146	32	output	output	NOUN
cana-1285	146	33	:	:	PUNCT
cana-1285	146	34	primary_loss	primary_los	NOUN
cana-1285	146	35	:	:	PUNCT
cana-1285	146	36	the	the	DET
cana-1285	146	37	computed	compute	VERB
cana-1285	146	38	loss	loss	NOUN
cana-1285	146	39	value	value	NOUN
cana-1285	146	40	based	base	VERB
cana-1285	146	41	on	on	ADP
cana-1285	146	42	the	the	DET
cana-1285	146	43	model	model	NOUN
cana-1285	146	44	's	's	PART
cana-1285	146	45	predictions	prediction	NOUN
cana-1285	146	46	and	and	CCONJ
cana-1285	146	47	the	the	DET
cana-1285	146	48	true	true	ADJ
cana-1285	146	49	target	target	NOUN
cana-1285	146	50	values	value	NOUN
cana-1285	146	51	#	#	NOUN
cana-1285	146	52	extract	extract	VERB
cana-1285	146	53	input	input	NOUN
cana-1285	146	54	features	feature	NOUN
cana-1285	146	55	and	and	CCONJ
cana-1285	146	56	target	target	VERB
cana-1285	146	57	values	value	NOUN
cana-1285	146	58	from	from	ADP
cana-1285	146	59	data	datum	NOUN
cana-1285	146	60	features	feature	NOUN
cana-1285	146	61	,	,	PUNCT
cana-1285	146	62	targets	target	NOUN
cana-1285	146	63	=	=	SYM
cana-1285	146	64	extractfeaturesandtargets(data	extractfeaturesandtargets(data	PROPN
cana-1285	146	65	)	)	PUNCT
cana-1285	146	66	#	#	NOUN
cana-1285	146	67	get	get	VERB
cana-1285	146	68	model	model	NOUN
cana-1285	146	69	predictions	prediction	NOUN
cana-1285	146	70	predictions	prediction	NOUN
cana-1285	146	71	=	=	SYM
cana-1285	146	72	model.predict(features	model.predict(feature	NOUN
cana-1285	146	73	)	)	PUNCT
cana-1285	146	74	#	#	NOUN
cana-1285	146	75	compute	compute	NOUN
cana-1285	146	76	the	the	DET
cana-1285	146	77	primary	primary	ADJ
cana-1285	146	78	loss	loss	NOUN
cana-1285	146	79	(	(	PUNCT
cana-1285	146	80	e.g.	e.g.	ADV
cana-1285	146	81	,	,	PUNCT
cana-1285	146	82	mean	mean	VERB
cana-1285	146	83	squared	square	VERB
cana-1285	146	84	error	error	NOUN
cana-1285	146	85	,	,	PUNCT
cana-1285	146	86	cross	cross	NOUN
cana-1285	146	87	-	-	NOUN
cana-1285	146	88	entropy	entropy	ADJ
cana-1285	146	89	)	)	PUNCT
cana-1285	146	90	primary_loss	primary_los	NOUN
cana-1285	146	91	=	=	SYM
cana-1285	146	92	calculateloss(predictions	calculateloss(prediction	NOUN
cana-1285	146	93	,	,	PUNCT
cana-1285	146	94	targets	target	NOUN
cana-1285	146	95	)	)	PUNCT
cana-1285	146	96	return	return	VERB
cana-1285	146	97	primary_loss	primary_los	NOUN
cana-1285	146	98	function	function	PROPN
cana-1285	146	99	extractfeaturesandtargets(data	extractfeaturesandtargets(data	PROPN
cana-1285	146	100	):	):	PUNCT
cana-1285	146	101	input	input	NOUN
cana-1285	146	102	:	:	PUNCT
cana-1285	146	103	data	datum	NOUN
cana-1285	146	104	:	:	PUNCT
cana-1285	146	105	dataset	dataset	NOUN
cana-1285	146	106	consisting	consist	VERB
cana-1285	146	107	of	of	ADP
cana-1285	146	108	input	input	NOUN
cana-1285	146	109	features	feature	NOUN
cana-1285	146	110	and	and	CCONJ
cana-1285	146	111	corresponding	correspond	VERB
cana-1285	146	112	target	target	NOUN
cana-1285	146	113	values	value	NOUN
cana-1285	146	114	output	output	NOUN
cana-1285	146	115	:	:	PUNCT
cana-1285	146	116	features	feature	NOUN
cana-1285	146	117	:	:	PUNCT
cana-1285	146	118	input	input	NOUN
cana-1285	146	119	features	feature	VERB
cana-1285	146	120	targets	target	NOUN
cana-1285	146	121	:	:	PUNCT
cana-1285	146	122	target	target	NOUN
cana-1285	146	123	values	value	NOUN
cana-1285	146	124	#	#	NOUN
cana-1285	146	125	extract	extract	VERB
cana-1285	146	126	input	input	NOUN
cana-1285	146	127	features	feature	NOUN
cana-1285	146	128	and	and	CCONJ
cana-1285	146	129	target	target	VERB
cana-1285	146	130	values	value	NOUN
cana-1285	146	131	from	from	ADP
cana-1285	146	132	data	datum	NOUN
cana-1285	146	133	features	feature	NOUN
cana-1285	146	134	=	=	NOUN
cana-1285	146	135	data.features	data.feature	NOUN
cana-1285	146	136	targets	target	NOUN
cana-1285	146	137	=	=	SYM
cana-1285	146	138	data.targets	data.target	NOUN
cana-1285	146	139	return	return	VERB
cana-1285	146	140	features	feature	NOUN
cana-1285	146	141	,	,	PUNCT
cana-1285	146	142	targets	target	NOUN
cana-1285	146	143	function	function	VERB
cana-1285	146	144	calculateloss(predictions	calculateloss(prediction	NOUN
cana-1285	146	145	,	,	PUNCT
cana-1285	146	146	targets	target	NOUN
cana-1285	146	147	):	):	PUNCT
cana-1285	146	148	input	input	NOUN
cana-1285	146	149	:	:	PUNCT
cana-1285	146	150	predictions	prediction	NOUN
cana-1285	146	151	:	:	PUNCT
cana-1285	146	152	model	model	NOUN
cana-1285	146	153	predictions	prediction	NOUN
cana-1285	146	154	targets	target	NOUN
cana-1285	146	155	:	:	PUNCT
cana-1285	146	156	true	true	ADJ
cana-1285	146	157	target	target	NOUN
cana-1285	146	158	values	value	NOUN
cana-1285	146	159	output	output	NOUN
cana-1285	146	160	:	:	PUNCT
cana-1285	146	161	loss	loss	NOUN
cana-1285	146	162	:	:	PUNCT
cana-1285	146	163	computed	compute	VERB
cana-1285	146	164	loss	loss	NOUN
cana-1285	146	165	value	value	NOUN
cana-1285	146	166	communications	communication	NOUN
cana-1285	146	167	on	on	ADP
cana-1285	146	168	applied	apply	VERB
cana-1285	146	169	nonlinear	nonlinear	ADJ
cana-1285	146	170	analysis	analysis	NOUN
cana-1285	146	171	issn	issn	NOUN
cana-1285	146	172	:	:	PUNCT
cana-1285	146	173	1074	1074	NUM
cana-1285	146	174	-	-	PUNCT
cana-1285	146	175	133x	133x	NUM
cana-1285	146	176	vol	vol	NOUN
cana-1285	146	177	31	31	NUM
cana-1285	146	178	no	no	NOUN
cana-1285	146	179	.	.	PUNCT
cana-1285	147	1	7s	7	NOUN
cana-1285	147	2	(	(	PUNCT
cana-1285	147	3	2024	2024	NUM
cana-1285	147	4	)	)	PUNCT
cana-1285	147	5	76	76	NUM
cana-1285	147	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1285	147	7	#	#	NOUN
cana-1285	147	8	compute	compute	NOUN
cana-1285	147	9	the	the	DET
cana-1285	147	10	loss	loss	NOUN
cana-1285	147	11	(	(	PUNCT
cana-1285	147	12	e.g.	e.g.	ADV
cana-1285	147	13	,	,	PUNCT
cana-1285	147	14	mean	mean	VERB
cana-1285	147	15	squared	square	VERB
cana-1285	147	16	error	error	NOUN
cana-1285	147	17	)	)	PUNCT
cana-1285	147	18	loss	loss	NOUN
cana-1285	147	19	=	=	SYM
cana-1285	147	20	meansquarederror(predictions	meansquarederror(prediction	NOUN
cana-1285	147	21	,	,	PUNCT
cana-1285	147	22	targets	target	NOUN
cana-1285	147	23	)	)	PUNCT
cana-1285	147	24	return	return	VERB
cana-1285	147	25	loss	loss	NOUN
cana-1285	147	26	function	function	NOUN
cana-1285	147	27	meansquarederror(predictions	meansquarederror(prediction	NOUN
cana-1285	147	28	,	,	PUNCT
cana-1285	147	29	targets	target	NOUN
cana-1285	147	30	):	):	PUNCT
cana-1285	147	31	input	input	NOUN
cana-1285	147	32	:	:	PUNCT
cana-1285	147	33	predictions	prediction	NOUN
cana-1285	147	34	:	:	PUNCT
cana-1285	147	35	model	model	NOUN
cana-1285	147	36	predictions	prediction	NOUN
cana-1285	147	37	targets	target	NOUN
cana-1285	147	38	:	:	PUNCT
cana-1285	147	39	true	true	ADJ
cana-1285	147	40	target	target	NOUN
cana-1285	147	41	values	value	NOUN
cana-1285	147	42	output	output	NOUN
cana-1285	147	43	:	:	PUNCT
cana-1285	147	44	mse	mse	NOUN
cana-1285	147	45	:	:	PUNCT
cana-1285	147	46	mean	mean	VERB
cana-1285	147	47	squared	square	VERB
cana-1285	147	48	error	error	NOUN
cana-1285	147	49	#	#	NOUN
cana-1285	147	50	calculate	calculate	NOUN
cana-1285	147	51	mean	mean	VERB
cana-1285	147	52	squared	square	VERB
cana-1285	147	53	error	error	NOUN
cana-1285	147	54	mse	mse	NOUN
cana-1285	147	55	=	=	PUNCT
cana-1285	147	56	average((predictions	average((predictions	PROPN
cana-1285	147	57	targets)^2	targets)^2	PROPN
cana-1285	147	58	)	)	PUNCT
cana-1285	147	59	return	return	NOUN
cana-1285	147	60	mse	mse	NOUN
cana-1285	147	61	7	7	NUM
cana-1285	147	62	.	.	PUNCT
cana-1285	147	63	model	model	NOUN
cana-1285	147	64	training	training	NOUN
cana-1285	147	65	combining	combine	VERB
cana-1285	147	66	nonlinear	nonlinear	ADJ
cana-1285	147	67	dynamics	dynamic	NOUN
cana-1285	147	68	with	with	ADP
cana-1285	147	69	deep	deep	ADJ
cana-1285	147	70	learning	learning	NOUN
cana-1285	147	71	,	,	PUNCT
cana-1285	147	72	the	the	DET
cana-1285	147	73	proposed	propose	VERB
cana-1285	147	74	method	method	NOUN
cana-1285	147	75	trains	train	NOUN
cana-1285	147	76	to	to	PART
cana-1285	147	77	maximize	maximize	VERB
cana-1285	147	78	a	a	DET
cana-1285	147	79	neural	neural	ADJ
cana-1285	147	80	network	network	NOUN
cana-1285	147	81	to	to	PART
cana-1285	147	82	learn	learn	VERB
cana-1285	147	83	both	both	DET
cana-1285	147	84	adherence	adherence	NOUN
cana-1285	147	85	to	to	ADP
cana-1285	147	86	the	the	DET
cana-1285	147	87	dynamic	dynamic	ADJ
cana-1285	147	88	system	system	NOUN
cana-1285	147	89	constraints	constraint	NOUN
cana-1285	147	90	and	and	CCONJ
cana-1285	147	91	forecast	forecast	NOUN
cana-1285	147	92	accuracy	accuracy	NOUN
cana-1285	147	93	.	.	PUNCT
cana-1285	148	1	this	this	DET
cana-1285	148	2	approach	approach	NOUN
cana-1285	148	3	is	be	AUX
cana-1285	148	4	crucial	crucial	ADJ
cana-1285	148	5	to	to	PART
cana-1285	148	6	ensure	ensure	VERB
cana-1285	148	7	that	that	SCONJ
cana-1285	148	8	the	the	DET
cana-1285	148	9	model	model	NOUN
cana-1285	148	10	catches	catch	VERB
cana-1285	148	11	the	the	DET
cana-1285	148	12	complex	complex	ADJ
cana-1285	148	13	interactions	interaction	NOUN
cana-1285	148	14	in	in	ADP
cana-1285	148	15	biological	biological	ADJ
cana-1285	148	16	data	datum	NOUN
cana-1285	148	17	and	and	CCONJ
cana-1285	148	18	respects	respect	VERB
cana-1285	148	19	the	the	DET
cana-1285	148	20	basic	basic	ADJ
cana-1285	148	21	dynamics	dynamic	NOUN
cana-1285	148	22	stated	state	VERB
cana-1285	148	23	by	by	ADP
cana-1285	148	24	nonlinear	nonlinear	ADJ
cana-1285	148	25	differential	differential	ADJ
cana-1285	148	26	equations	equation	NOUN
cana-1285	148	27	.	.	PUNCT
cana-1285	149	1	the	the	DET
cana-1285	149	2	training	training	NOUN
cana-1285	149	3	approach	approach	NOUN
cana-1285	149	4	balances	balance	VERB
cana-1285	149	5	two	two	NUM
cana-1285	149	6	major	major	ADJ
cana-1285	149	7	goals	goal	NOUN
cana-1285	149	8	by	by	ADP
cana-1285	149	9	first	first	ADV
cana-1285	149	10	building	build	VERB
cana-1285	149	11	a	a	DET
cana-1285	149	12	comprehensive	comprehensive	ADJ
cana-1285	149	13	loss	loss	NOUN
cana-1285	149	14	function	function	NOUN
cana-1285	149	15	that	that	PRON
cana-1285	149	16	guarantees	guarantee	VERB
cana-1285	149	17	compliance	compliance	NOUN
cana-1285	149	18	with	with	ADP
cana-1285	149	19	dynamic	dynamic	ADJ
cana-1285	149	20	system	system	NOUN
cana-1285	149	21	constraints	constraint	NOUN
cana-1285	149	22	and	and	CCONJ
cana-1285	149	23	reduces	reduce	VERB
cana-1285	149	24	prediction	prediction	NOUN
cana-1285	149	25	error	error	NOUN
cana-1285	149	26	.	.	PUNCT
cana-1285	150	1	by	by	ADP
cana-1285	150	2	use	use	NOUN
cana-1285	150	3	of	of	ADP
cana-1285	150	4	gradient	gradient	NOUN
cana-1285	150	5	-	-	PUNCT
cana-1285	150	6	based	base	VERB
cana-1285	150	7	optimization	optimization	NOUN
cana-1285	150	8	approaches	approach	VERB
cana-1285	150	9	as	as	ADP
cana-1285	150	10	stochastic	stochastic	ADJ
cana-1285	150	11	gradient	gradient	ADJ
cana-1285	150	12	descent	descent	NOUN
cana-1285	150	13	(	(	PUNCT
cana-1285	150	14	sgd	sgd	NOUN
cana-1285	150	15	)	)	PUNCT
cana-1285	150	16	or	or	CCONJ
cana-1285	150	17	adam	adam	PROPN
cana-1285	150	18	,	,	PUNCT
cana-1285	150	19	we	we	PRON
cana-1285	150	20	minimize	minimize	VERB
cana-1285	150	21	the	the	DET
cana-1285	150	22	total	total	ADJ
cana-1285	150	23	loss	loss	NOUN
cana-1285	150	24	function	function	NOUN
cana-1285	150	25	thereby	thereby	ADV
cana-1285	150	26	training	train	VERB
cana-1285	150	27	the	the	DET
cana-1285	150	28	model	model	NOUN
cana-1285	150	29	.	.	PUNCT
cana-1285	151	1	reducing	reduce	VERB
cana-1285	151	2	the	the	DET
cana-1285	151	3	total	total	ADJ
cana-1285	151	4	loss	loss	NOUN
cana-1285	151	5	will	will	AUX
cana-1285	151	6	enable	enable	VERB
cana-1285	151	7	both	both	DET
cana-1285	151	8	compliance	compliance	NOUN
cana-1285	151	9	with	with	ADP
cana-1285	151	10	dynamic	dynamic	ADJ
cana-1285	151	11	limits	limit	NOUN
cana-1285	151	12	and	and	CCONJ
cana-1285	151	13	better	well	ADJ
cana-1285	151	14	projected	project	VERB
cana-1285	151	15	accuracy	accuracy	NOUN
cana-1285	151	16	.	.	PUNCT
cana-1285	152	1	usually	usually	ADV
cana-1285	152	2	,	,	PUNCT
cana-1285	152	3	the	the	DET
cana-1285	152	4	loss	loss	NOUN
cana-1285	152	5	values	value	NOUN
cana-1285	152	6	over	over	ADP
cana-1285	152	7	several	several	ADJ
cana-1285	152	8	epochs	epoch	NOUN
cana-1285	152	9	enable	enable	VERB
cana-1285	152	10	one	one	NUM
cana-1285	152	11	to	to	PART
cana-1285	152	12	check	check	VERB
cana-1285	152	13	convergence	convergence	NOUN
cana-1285	152	14	.	.	PUNCT
cana-1285	153	1	training	training	NOUN
cana-1285	153	2	suggests	suggest	VERB
cana-1285	153	3	that	that	SCONJ
cana-1285	153	4	the	the	DET
cana-1285	153	5	model	model	NOUN
cana-1285	153	6	has	have	AUX
cana-1285	153	7	learnt	learn	VERB
cana-1285	153	8	to	to	PART
cana-1285	153	9	balance	balance	VERB
cana-1285	153	10	adherence	adherence	NOUN
cana-1285	153	11	to	to	ADP
cana-1285	153	12	the	the	DET
cana-1285	153	13	dynamic	dynamic	ADJ
cana-1285	153	14	system	system	NOUN
cana-1285	153	15	equations	equation	NOUN
cana-1285	153	16	with	with	ADP
cana-1285	153	17	prediction	prediction	NOUN
cana-1285	153	18	accuracy	accuracy	NOUN
cana-1285	153	19	since	since	SCONJ
cana-1285	153	20	it	it	PRON
cana-1285	153	21	keeps	keep	VERB
cana-1285	153	22	till	till	SCONJ
cana-1285	153	23	the	the	DET
cana-1285	153	24	loss	loss	NOUN
cana-1285	153	25	stabilizes	stabilize	VERB
cana-1285	153	26	or	or	CCONJ
cana-1285	153	27	goes	go	VERB
cana-1285	153	28	below	below	ADP
cana-1285	153	29	a	a	DET
cana-1285	153	30	predefined	predefine	VERB
cana-1285	153	31	threshold	threshold	NOUN
cana-1285	153	32	.	.	PUNCT
cana-1285	154	1	through	through	ADP
cana-1285	154	2	dynamic	dynamic	ADJ
cana-1285	154	3	constraints	constraint	NOUN
cana-1285	154	4	included	include	VERB
cana-1285	154	5	into	into	ADP
cana-1285	154	6	the	the	DET
cana-1285	154	7	training	training	NOUN
cana-1285	154	8	process	process	NOUN
cana-1285	154	9	,	,	PUNCT
cana-1285	154	10	the	the	DET
cana-1285	154	11	model	model	NOUN
cana-1285	154	12	not	not	PART
cana-1285	154	13	only	only	ADV
cana-1285	154	14	learns	learn	VERB
cana-1285	154	15	to	to	PART
cana-1285	154	16	generate	generate	VERB
cana-1285	154	17	accurate	accurate	ADJ
cana-1285	154	18	predictions	prediction	NOUN
cana-1285	154	19	but	but	CCONJ
cana-1285	154	20	also	also	ADV
cana-1285	154	21	respects	respect	VERB
cana-1285	154	22	the	the	DET
cana-1285	154	23	underlying	underlying	ADJ
cana-1285	154	24	nonlinear	nonlinear	ADJ
cana-1285	154	25	dynamics	dynamic	NOUN
cana-1285	154	26	,	,	PUNCT
cana-1285	154	27	so	so	ADV
cana-1285	154	28	strengthening	strengthen	VERB
cana-1285	154	29	and	and	CCONJ
cana-1285	154	30	interpretable	interpretable	ADJ
cana-1285	154	31	analysis	analysis	NOUN
cana-1285	154	32	of	of	ADP
cana-1285	154	33	biological	biological	ADJ
cana-1285	154	34	components	component	NOUN
cana-1285	154	35	.	.	PUNCT
cana-1285	155	1	8	8	X
cana-1285	155	2	.	.	PUNCT
cana-1285	155	3	performance	performance	NOUN
cana-1285	155	4	assessment	assessment	NOUN
cana-1285	155	5	extensive	extensive	ADJ
cana-1285	155	6	simulations	simulation	NOUN
cana-1285	155	7	and	and	CCONJ
cana-1285	155	8	analogues	analogue	NOUN
cana-1285	155	9	with	with	ADP
cana-1285	155	10	existing	exist	VERB
cana-1285	155	11	methods	method	NOUN
cana-1285	155	12	offer	offer	VERB
cana-1285	155	13	the	the	DET
cana-1285	155	14	experimental	experimental	ADJ
cana-1285	155	15	framework	framework	NOUN
cana-1285	155	16	to	to	PART
cana-1285	155	17	evaluate	evaluate	VERB
cana-1285	155	18	the	the	DET
cana-1285	155	19	proposed	propose	VERB
cana-1285	155	20	idea	idea	NOUN
cana-1285	155	21	.	.	PUNCT
cana-1285	156	1	mostly	mostly	ADV
cana-1285	156	2	with	with	ADP
cana-1285	156	3	tensorflow	tensorflow	NOUN
cana-1285	156	4	and	and	CCONJ
cana-1285	156	5	pytorch	pytorch	NOUN
cana-1285	156	6	as	as	ADP
cana-1285	156	7	the	the	DET
cana-1285	156	8	main	main	ADJ
cana-1285	156	9	simulation	simulation	NOUN
cana-1285	156	10	tool	tool	NOUN
cana-1285	156	11	,	,	PUNCT
cana-1285	156	12	we	we	PRON
cana-1285	156	13	developed	develop	VERB
cana-1285	156	14	and	and	CCONJ
cana-1285	156	15	trained	train	VERB
cana-1285	156	16	the	the	DET
cana-1285	156	17	deep	deep	ADJ
cana-1285	156	18	learning	learning	NOUN
cana-1285	156	19	models	model	NOUN
cana-1285	156	20	.	.	PUNCT
cana-1285	157	1	the	the	DET
cana-1285	157	2	computational	computational	ADJ
cana-1285	157	3	research	research	NOUN
cana-1285	157	4	managed	manage	VERB
cana-1285	157	5	the	the	DET
cana-1285	157	6	demanding	demanding	ADJ
cana-1285	157	7	calculations	calculation	NOUN
cana-1285	157	8	the	the	DET
cana-1285	157	9	dynamic	dynamic	ADJ
cana-1285	157	10	deep	deep	ADJ
cana-1285	157	11	neural	neural	ADJ
cana-1285	157	12	network	network	NOUN
cana-1285	157	13	(	(	PUNCT
cana-1285	157	14	dnn	dnn	PROPN
cana-1285	157	15	)	)	PUNCT
cana-1285	157	16	demands	demand	NOUN
cana-1285	157	17	by	by	ADP
cana-1285	157	18	using	use	VERB
cana-1285	157	19	high	high	ADJ
cana-1285	157	20	-	-	PUNCT
cana-1285	157	21	performance	performance	NOUN
cana-1285	157	22	processors	processor	NOUN
cana-1285	157	23	running	run	VERB
cana-1285	157	24	nvidia	nvidia	PROPN
cana-1285	157	25	rtx	rtx	PROPN
cana-1285	157	26	3090	3090	NUM
cana-1285	157	27	gpus	gpu	NOUN
cana-1285	157	28	.	.	PUNCT
cana-1285	158	1	each	each	DET
cana-1285	158	2	model	model	NOUN
cana-1285	158	3	was	be	AUX
cana-1285	158	4	trained	train	VERB
cana-1285	158	5	on	on	ADP
cana-1285	158	6	biochemical	biochemical	ADJ
cana-1285	158	7	datasets	dataset	NOUN
cana-1285	158	8	with	with	ADP
cana-1285	158	9	varying	vary	VERB
cana-1285	158	10	sample	sample	NOUN
cana-1285	158	11	sizes—150	sizes—150	PROPN
cana-1285	158	12	,	,	PUNCT
cana-1285	158	13	300	300	NUM
cana-1285	158	14	,	,	PUNCT
cana-1285	158	15	450	450	NUM
cana-1285	158	16	,	,	PUNCT
cana-1285	158	17	and	and	CCONJ
cana-1285	158	18	600	600	NUM
cana-1285	158	19	samples	sample	NOUN
cana-1285	158	20	—	—	PUNCT
cana-1285	158	21	in	in	ADP
cana-1285	158	22	order	order	NOUN
cana-1285	158	23	to	to	PART
cana-1285	158	24	assess	assess	VERB
cana-1285	158	25	performance	performance	NOUN
cana-1285	158	26	over	over	ADP
cana-1285	158	27	numerous	numerous	ADJ
cana-1285	158	28	data	datum	NOUN
cana-1285	158	29	scales	scale	NOUN
cana-1285	158	30	.	.	PUNCT
cana-1285	159	1	we	we	PRON
cana-1285	159	2	employed	employ	VERB
cana-1285	159	3	a	a	DET
cana-1285	159	4	5	5	NUM
cana-1285	159	5	-	-	ADJ
cana-1285	159	6	fold	fold	ADJ
cana-1285	159	7	cross	cross	ADJ
cana-1285	159	8	-	-	ADJ
cana-1285	159	9	valuation	valuation	ADJ
cana-1285	159	10	technique	technique	NOUN
cana-1285	159	11	to	to	PART
cana-1285	159	12	assure	assure	VERB
cana-1285	159	13	resilience	resilience	NOUN
cana-1285	159	14	and	and	CCONJ
cana-1285	159	15	generalizability	generalizability	NOUN
cana-1285	159	16	of	of	ADP
cana-1285	159	17	the	the	DET
cana-1285	159	18	outcomes	outcome	NOUN
cana-1285	159	19	with	with	ADP
cana-1285	159	20	each	each	DET
cana-1285	159	21	fold	fold	NOUN
cana-1285	159	22	having	have	VERB
cana-1285	159	23	unique	unique	ADJ
cana-1285	159	24	training	training	NOUN
cana-1285	159	25	and	and	CCONJ
cana-1285	159	26	validation	validation	NOUN
cana-1285	159	27	periods	period	NOUN
cana-1285	159	28	.	.	PUNCT
cana-1285	160	1	using	use	VERB
cana-1285	160	2	mean	mean	NOUN
cana-1285	160	3	squared	square	VERB
cana-1285	160	4	error	error	NOUN
cana-1285	160	5	,	,	PUNCT
cana-1285	160	6	variance	variance	NOUN
cana-1285	160	7	explained	explain	VERB
cana-1285	160	8	assessed	assessed	ADJ
cana-1285	160	9	model	model	NOUN
cana-1285	160	10	interpretability	interpretability	NOUN
cana-1285	160	11	;	;	PUNCT
cana-1285	160	12	and	and	CCONJ
cana-1285	160	13	computational	computational	ADJ
cana-1285	160	14	efficiency	efficiency	NOUN
cana-1285	160	15	(	(	PUNCT
cana-1285	160	16	measured	measure	VERB
cana-1285	160	17	by	by	ADP
cana-1285	160	18	training	training	NOUN
cana-1285	160	19	time	time	NOUN
cana-1285	160	20	and	and	CCONJ
cana-1285	160	21	inference	inference	NOUN
cana-1285	160	22	speed	speed	NOUN
cana-1285	160	23	)	)	PUNCT
cana-1285	160	24	.	.	PUNCT
cana-1285	161	1	deepbio	deepbio	NOUN
cana-1285	161	2	,	,	PUNCT
cana-1285	161	3	a	a	DET
cana-1285	161	4	deep	deep	ADJ
cana-1285	161	5	learning	learning	NOUN
cana-1285	161	6	framework	framework	NOUN
cana-1285	161	7	communications	communication	NOUN
cana-1285	161	8	on	on	ADP
cana-1285	161	9	applied	apply	VERB
cana-1285	161	10	nonlinear	nonlinear	ADJ
cana-1285	161	11	analysis	analysis	NOUN
cana-1285	161	12	issn	issn	NOUN
cana-1285	161	13	:	:	PUNCT
cana-1285	161	14	1074	1074	NUM
cana-1285	161	15	-	-	PUNCT
cana-1285	161	16	133x	133x	NUM
cana-1285	161	17	vol	vol	NOUN
cana-1285	161	18	31	31	NUM
cana-1285	161	19	no	no	NOUN
cana-1285	161	20	.	.	PUNCT
cana-1285	162	1	7s	7	NOUN
cana-1285	162	2	(	(	PUNCT
cana-1285	162	3	2024	2024	NUM
cana-1285	162	4	)	)	PUNCT
cana-1285	162	5	77	77	NUM
cana-1285	162	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1285	162	7	optimized	optimize	VERB
cana-1285	162	8	for	for	ADP
cana-1285	162	9	biological	biological	ADJ
cana-1285	162	10	data	datum	NOUN
cana-1285	162	11	analysis	analysis	NOUN
cana-1285	162	12	;	;	PUNCT
cana-1285	162	13	unidl4biopep	unidl4biopep	NOUN
cana-1285	162	14	,	,	PUNCT
cana-1285	162	15	a	a	DET
cana-1285	162	16	deep	deep	ADJ
cana-1285	162	17	learning	learning	NOUN
cana-1285	162	18	model	model	NOUN
cana-1285	162	19	tailored	tailor	VERB
cana-1285	162	20	for	for	ADP
cana-1285	162	21	peptide	peptide	ADJ
cana-1285	162	22	data	datum	NOUN
cana-1285	162	23	;	;	PUNCT
cana-1285	162	24	and	and	CCONJ
cana-1285	162	25	neural	neural	ADJ
cana-1285	162	26	network	network	NOUN
cana-1285	162	27	language	language	NOUN
cana-1285	162	28	model	model	NOUN
cana-1285	162	29	(	(	PUNCT
cana-1285	162	30	nnlm	nnlm	PROPN
cana-1285	162	31	)	)	PUNCT
cana-1285	162	32	,	,	PUNCT
cana-1285	162	33	which	which	PRON
cana-1285	162	34	integrates	integrate	VERB
cana-1285	162	35	sequence	sequence	NOUN
cana-1285	162	36	data	datum	NOUN
cana-1285	162	37	for	for	ADP
cana-1285	162	38	biochemical	biochemical	ADJ
cana-1285	162	39	predictions	prediction	NOUN
cana-1285	162	40	,	,	PUNCT
cana-1285	162	41	were	be	AUX
cana-1285	162	42	evaluated	evaluate	VERB
cana-1285	162	43	against	against	ADP
cana-1285	162	44	the	the	DET
cana-1285	162	45	results	result	NOUN
cana-1285	162	46	of	of	ADP
cana-1285	162	47	the	the	DET
cana-1285	162	48	proposed	propose	VERB
cana-1285	162	49	model	model	NOUN
cana-1285	162	50	.	.	PUNCT
cana-1285	163	1	table	table	NOUN
cana-1285	163	2	2	2	NUM
cana-1285	163	3	:	:	PUNCT
cana-1285	163	4	experimental	experimental	ADJ
cana-1285	163	5	setup	setup	NOUN
cana-1285	163	6	/	/	SYM
cana-1285	163	7	parameters	parameter	NOUN
cana-1285	163	8	parameter	parameter	NOUN
cana-1285	163	9	value	value	NOUN
cana-1285	163	10	simulation	simulation	NOUN
cana-1285	163	11	tools	tool	NOUN
cana-1285	163	12	tensorflow	tensorflow	VERB
cana-1285	163	13	,	,	PUNCT
cana-1285	163	14	pytorch	pytorch	NOUN
cana-1285	163	15	gpus	gpu	NOUN
cana-1285	163	16	used	use	VERB
cana-1285	163	17	nvidia	nvidia	PROPN
cana-1285	163	18	rtx	rtx	PROPN
cana-1285	163	19	3090	3090	NUM
cana-1285	163	20	cpu	cpu	NOUN
cana-1285	163	21	used	use	VERB
cana-1285	163	22	intel	intel	PROPN
cana-1285	163	23	i7	i7	PROPN
cana-1285	163	24	ram	ram	NOUN
cana-1285	163	25	128	128	NUM
cana-1285	163	26	gb	gb	NOUN
cana-1285	163	27	ddr4	ddr4	NOUN
cana-1285	163	28	dataset	dataset	VERB
cana-1285	163	29	sizes	size	NOUN
cana-1285	163	30	150	150	NUM
cana-1285	163	31	,	,	PUNCT
cana-1285	163	32	300	300	NUM
cana-1285	163	33	,	,	PUNCT
cana-1285	163	34	450	450	NUM
cana-1285	163	35	,	,	PUNCT
cana-1285	163	36	600	600	NUM
cana-1285	163	37	samples	sample	NOUN
cana-1285	163	38	training	training	NOUN
cana-1285	163	39	epochs	epoch	NOUN
cana-1285	163	40	100	100	NUM
cana-1285	163	41	batch	batch	NOUN
cana-1285	163	42	size	size	NOUN
cana-1285	163	43	64	64	NUM
cana-1285	163	44	learning	learning	NOUN
cana-1285	163	45	rate	rate	NOUN
cana-1285	163	46	0.001	0.001	NUM
cana-1285	163	47	λ	λ	NOUN
cana-1285	163	48	0.1	0.1	NUM
cana-1285	163	49	optimization	optimization	NOUN
cana-1285	163	50	algorithm	algorithm	NOUN
cana-1285	163	51	adam	adam	PROPN
cana-1285	163	52	activation	activation	NOUN
cana-1285	163	53	function	function	VERB
cana-1285	163	54	relu	relu	NOUN
cana-1285	163	55	cross	cross	NOUN
cana-1285	163	56	-	-	ADJ
cana-1285	163	57	validation	validation	ADJ
cana-1285	163	58	folds	fold	NOUN
cana-1285	163	59	5	5	NUM
cana-1285	163	60	9	9	NUM
cana-1285	163	61	.	.	PUNCT
cana-1285	164	1	performance	performance	NOUN
cana-1285	164	2	metrics	metric	NOUN
cana-1285	164	3	:	:	PUNCT
cana-1285	165	1	1	1	X
cana-1285	165	2	.	.	X
cana-1285	165	3	accuracy	accuracy	NOUN
cana-1285	165	4	(	(	PUNCT
cana-1285	165	5	mean	mean	INTJ
cana-1285	165	6	squared	square	VERB
cana-1285	165	7	error	error	NOUN
cana-1285	165	8	,	,	PUNCT
cana-1285	165	9	mse	mse	PROPN
cana-1285	165	10	):	):	PUNCT
cana-1285	165	11	sometimes	sometimes	ADV
cana-1285	165	12	referred	refer	VERB
cana-1285	165	13	to	to	ADP
cana-1285	165	14	as	as	ADV
cana-1285	165	15	mean	mean	VERB
cana-1285	165	16	squared	square	VERB
cana-1285	165	17	error	error	NOUN
cana-1285	165	18	,	,	PUNCT
cana-1285	165	19	mse	mse	NOUN
cana-1285	165	20	,	,	PUNCT
cana-1285	165	21	accuracy	accuracy	NOUN
cana-1285	165	22	measures	measure	NOUN
cana-1285	165	23	how	how	SCONJ
cana-1285	165	24	closely	closely	ADV
cana-1285	165	25	the	the	DET
cana-1285	165	26	model	model	NOUN
cana-1285	165	27	's	's	PART
cana-1285	165	28	forecasts	forecast	NOUN
cana-1285	165	29	match	match	VERB
cana-1285	165	30	actual	actual	ADJ
cana-1285	165	31	data	datum	NOUN
cana-1285	165	32	.	.	PUNCT
cana-1285	166	1	commonly	commonly	ADV
cana-1285	166	2	used	use	VERB
cana-1285	166	3	to	to	PART
cana-1285	166	4	gauge	gauge	VERB
cana-1285	166	5	this	this	PRON
cana-1285	166	6	for	for	ADP
cana-1285	166	7	regression	regression	NOUN
cana-1285	166	8	problems	problem	NOUN
cana-1285	166	9	is	be	AUX
cana-1285	166	10	the	the	DET
cana-1285	166	11	mean	mean	ADJ
cana-1285	166	12	squared	square	VERB
cana-1285	166	13	error	error	NOUN
cana-1285	166	14	(	(	PUNCT
cana-1285	166	15	mse	mse	NOUN
cana-1285	166	16	)	)	PUNCT
cana-1285	166	17	,	,	PUNCT
cana-1285	166	18	which	which	PRON
cana-1285	166	19	computes	compute	VERB
cana-1285	166	20	the	the	DET
cana-1285	166	21	average	average	ADJ
cana-1285	166	22	squared	squared	ADJ
cana-1285	166	23	difference	difference	NOUN
cana-1285	166	24	between	between	ADP
cana-1285	166	25	expected	expect	VERB
cana-1285	166	26	and	and	CCONJ
cana-1285	166	27	actual	actual	ADJ
cana-1285	166	28	data	datum	NOUN
cana-1285	166	29	.	.	PUNCT
cana-1285	167	1	reduced	reduce	VERB
cana-1285	167	2	mse	mse	PROPN
cana-1285	167	3	points	point	NOUN
cana-1285	167	4	to	to	ADP
cana-1285	167	5	better	well	ADV
cana-1285	167	6	predicting	predict	VERB
cana-1285	167	7	ability	ability	NOUN
cana-1285	167	8	.	.	PUNCT
cana-1285	168	1	2	2	NUM
cana-1285	168	2	1	1	NUM
cana-1285	168	3	1	1	NUM
cana-1285	168	4	ˆmse	ˆmse	NOUN
cana-1285	168	5	(	(	PUNCT
cana-1285	168	6	)	)	PUNCT
cana-1285	168	7	n	n	CCONJ
cana-1285	168	8	i	i	PRON
cana-1285	169	1	i	i	PRON
cana-1285	170	1	i	i	VERB
cana-1285	170	2	y	y	VERB
cana-1285	170	3	y	y	PROPN
cana-1285	170	4	n	n	NOUN
cana-1285	170	5	=	=	PUNCT
cana-1285	170	6	=	=	NOUN
cana-1285	170	7	−	−	NUM
cana-1285	170	8	2	2	NUM
cana-1285	170	9	.	.	X
cana-1285	171	1	interpretability	interpretability	NOUN
cana-1285	171	2	:	:	PUNCT
cana-1285	171	3	interpretability	interpretability	NOUN
cana-1285	171	4	of	of	ADP
cana-1285	171	5	a	a	DET
cana-1285	171	6	model	model	NOUN
cana-1285	171	7	is	be	AUX
cana-1285	171	8	the	the	DET
cana-1285	171	9	degree	degree	NOUN
cana-1285	171	10	to	to	PART
cana-1285	171	11	which	which	PRON
cana-1285	171	12	its	its	PRON
cana-1285	171	13	predictions	prediction	NOUN
cana-1285	171	14	and	and	CCONJ
cana-1285	171	15	behaviors	behavior	NOUN
cana-1285	171	16	make	make	VERB
cana-1285	171	17	sense	sense	NOUN
cana-1285	171	18	.	.	PUNCT
cana-1285	172	1	this	this	PRON
cana-1285	172	2	means	means	AUX
cana-1285	172	3	often	often	ADV
cana-1285	172	4	examining	examine	VERB
cana-1285	172	5	how	how	SCONJ
cana-1285	172	6	well	well	ADV
cana-1285	172	7	the	the	DET
cana-1285	172	8	model	model	NOUN
cana-1285	172	9	follows	follow	VERB
cana-1285	172	10	the	the	DET
cana-1285	172	11	basic	basic	ADJ
cana-1285	172	12	dynamic	dynamic	ADJ
cana-1285	172	13	system	system	NOUN
cana-1285	172	14	equations	equation	NOUN
cana-1285	172	15	.	.	PUNCT
cana-1285	173	1	improved	improve	VERB
cana-1285	173	2	interpretability	interpretability	NOUN
cana-1285	173	3	makes	make	VERB
cana-1285	173	4	comprehension	comprehension	NOUN
cana-1285	173	5	easier	easy	ADJ
cana-1285	173	6	and	and	CCONJ
cana-1285	173	7	encourages	encourage	VERB
cana-1285	173	8	one	one	NUM
cana-1285	173	9	to	to	PART
cana-1285	173	10	trust	trust	VERB
cana-1285	173	11	the	the	DET
cana-1285	173	12	outputs	output	NOUN
cana-1285	173	13	of	of	ADP
cana-1285	173	14	the	the	DET
cana-1285	173	15	model	model	NOUN
cana-1285	173	16	since	since	SCONJ
cana-1285	173	17	the	the	DET
cana-1285	173	18	predictions	prediction	NOUN
cana-1285	173	19	of	of	ADP
cana-1285	173	20	the	the	DET
cana-1285	173	21	model	model	NOUN
cana-1285	173	22	fit	fit	ADJ
cana-1285	173	23	known	know	VERB
cana-1285	173	24	dynamics	dynamic	NOUN
cana-1285	173	25	.	.	PUNCT
cana-1285	174	1	3	3	X
cana-1285	174	2	.	.	X
cana-1285	174	3	training	training	NOUN
cana-1285	174	4	time	time	NOUN
cana-1285	174	5	:	:	PUNCT
cana-1285	174	6	from	from	ADP
cana-1285	174	7	start	start	NOUN
cana-1285	174	8	to	to	ADP
cana-1285	174	9	finish	finish	NOUN
cana-1285	174	10	,	,	PUNCT
cana-1285	174	11	training	training	NOUN
cana-1285	174	12	time	time	NOUN
cana-1285	174	13	determines	determine	VERB
cana-1285	174	14	the	the	DET
cana-1285	174	15	required	required	ADJ
cana-1285	174	16	length	length	NOUN
cana-1285	174	17	to	to	PART
cana-1285	174	18	educate	educate	VERB
cana-1285	174	19	the	the	DET
cana-1285	174	20	model	model	NOUN
cana-1285	174	21	.	.	PUNCT
cana-1285	175	1	this	this	DET
cana-1285	175	2	statistic	statistic	NOUN
cana-1285	175	3	helps	help	VERB
cana-1285	175	4	one	one	PRON
cana-1285	175	5	evaluate	evaluate	VERB
cana-1285	175	6	the	the	DET
cana-1285	175	7	computing	computing	NOUN
cana-1285	175	8	efficiency	efficiency	NOUN
cana-1285	175	9	of	of	ADP
cana-1285	175	10	the	the	DET
cana-1285	175	11	model	model	NOUN
cana-1285	175	12	,	,	PUNCT
cana-1285	175	13	particularly	particularly	ADV
cana-1285	175	14	in	in	ADP
cana-1285	175	15	circumstances	circumstance	NOUN
cana-1285	175	16	of	of	ADP
cana-1285	175	17	complex	complex	ADJ
cana-1285	175	18	designs	design	NOUN
cana-1285	175	19	or	or	CCONJ
cana-1285	175	20	large	large	ADJ
cana-1285	175	21	datasets	dataset	NOUN
cana-1285	175	22	.	.	PUNCT
cana-1285	176	1	shortened	shorten	VERB
cana-1285	176	2	training	training	NOUN
cana-1285	176	3	periods	period	NOUN
cana-1285	176	4	indicate	indicate	VERB
cana-1285	176	5	a	a	DET
cana-1285	176	6	more	more	ADV
cana-1285	176	7	successful	successful	ADJ
cana-1285	176	8	model	model	NOUN
cana-1285	176	9	.	.	PUNCT
cana-1285	177	1	4	4	X
cana-1285	177	2	.	.	X
cana-1285	177	3	inference	inference	NOUN
cana-1285	177	4	speed	speed	NOUN
cana-1285	177	5	:	:	PUNCT
cana-1285	177	6	inference	inference	NOUN
cana-1285	177	7	speed	speed	NOUN
cana-1285	177	8	measures	measure	NOUN
cana-1285	177	9	,	,	PUNCT
cana-1285	177	10	after	after	SCONJ
cana-1285	177	11	trained	train	VERB
cana-1285	177	12	,	,	PUNCT
cana-1285	177	13	the	the	DET
cana-1285	177	14	predictive	predictive	ADJ
cana-1285	177	15	speed	speed	NOUN
cana-1285	177	16	of	of	ADP
cana-1285	177	17	the	the	DET
cana-1285	177	18	model	model	NOUN
cana-1285	177	19	.	.	PUNCT
cana-1285	178	1	it	it	PRON
cana-1285	178	2	is	be	AUX
cana-1285	178	3	absolutely	absolutely	ADV
cana-1285	178	4	essential	essential	ADJ
cana-1285	178	5	for	for	ADP
cana-1285	178	6	real	real	ADJ
cana-1285	178	7	-	-	PUNCT
cana-1285	178	8	time	time	NOUN
cana-1285	178	9	applications	application	NOUN
cana-1285	178	10	when	when	SCONJ
cana-1285	178	11	fast	fast	ADJ
cana-1285	178	12	reactions	reaction	NOUN
cana-1285	178	13	are	be	AUX
cana-1285	178	14	needed	need	VERB
cana-1285	178	15	.	.	PUNCT
cana-1285	179	1	faster	fast	ADJ
cana-1285	179	2	inference	inference	NOUN
cana-1285	179	3	speeds	speed	NOUN
cana-1285	179	4	allow	allow	VERB
cana-1285	179	5	one	one	NUM
cana-1285	179	6	decide	decide	VERB
cana-1285	179	7	and	and	CCONJ
cana-1285	179	8	carry	carry	VERB
cana-1285	179	9	out	out	ADP
cana-1285	179	10	actions	action	NOUN
cana-1285	179	11	more	more	ADV
cana-1285	179	12	quickly	quickly	ADV
cana-1285	179	13	.	.	PUNCT
cana-1285	180	1	5	5	X
cana-1285	180	2	.	.	X
cana-1285	180	3	variance	variance	NOUN
cana-1285	180	4	explained	explain	VERB
cana-1285	180	5	:	:	PUNCT
cana-1285	180	6	variance	variance	NOUN
cana-1285	180	7	explained	explain	VERB
cana-1285	180	8	,	,	PUNCT
cana-1285	180	9	which	which	PRON
cana-1285	180	10	measures	measure	VERB
cana-1285	180	11	the	the	DET
cana-1285	180	12	extent	extent	NOUN
cana-1285	180	13	of	of	ADP
cana-1285	180	14	data	datum	NOUN
cana-1285	180	15	fluctuation	fluctuation	NOUN
cana-1285	180	16	the	the	DET
cana-1285	180	17	model	model	NOUN
cana-1285	180	18	can	can	AUX
cana-1285	180	19	fairly	fairly	ADV
cana-1285	180	20	handle	handle	VERB
cana-1285	180	21	.	.	PUNCT
cana-1285	181	1	since	since	SCONJ
cana-1285	181	2	it	it	PRON
cana-1285	181	3	indicates	indicate	VERB
cana-1285	181	4	the	the	DET
cana-1285	181	5	percentage	percentage	NOUN
cana-1285	181	6	of	of	ADP
cana-1285	181	7	the	the	DET
cana-1285	181	8	variance	variance	NOUN
cana-1285	181	9	in	in	ADP
cana-1285	181	10	the	the	DET
cana-1285	181	11	dependent	dependent	ADJ
cana-1285	181	12	variable	variable	NOUN
cana-1285	181	13	that	that	PRON
cana-1285	181	14	communications	communication	NOUN
cana-1285	181	15	on	on	ADP
cana-1285	181	16	applied	apply	VERB
cana-1285	181	17	nonlinear	nonlinear	ADJ
cana-1285	181	18	analysis	analysis	NOUN
cana-1285	181	19	issn	issn	NOUN
cana-1285	181	20	:	:	PUNCT
cana-1285	181	21	1074	1074	NUM
cana-1285	181	22	-	-	PUNCT
cana-1285	181	23	133x	133x	NUM
cana-1285	181	24	vol	vol	NOUN
cana-1285	181	25	31	31	NUM
cana-1285	181	26	no	no	NOUN
cana-1285	181	27	.	.	PUNCT
cana-1285	182	1	7s	7	NOUN
cana-1285	182	2	(	(	PUNCT
cana-1285	182	3	2024	2024	NUM
cana-1285	182	4	)	)	PUNCT
cana-1285	182	5	78	78	NUM
cana-1285	182	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1285	182	7	might	might	AUX
cana-1285	182	8	be	be	AUX
cana-1285	182	9	predicted	predict	VERB
cana-1285	182	10	from	from	ADP
cana-1285	182	11	the	the	DET
cana-1285	182	12	independent	independent	ADJ
cana-1285	182	13	elements	element	NOUN
cana-1285	182	14	,	,	PUNCT
cana-1285	182	15	r2	r2	PROPN
cana-1285	182	16	is	be	AUX
cana-1285	182	17	one	one	NUM
cana-1285	182	18	of	of	ADP
cana-1285	182	19	the	the	DET
cana-1285	182	20	often	often	ADV
cana-1285	182	21	used	use	VERB
cana-1285	182	22	metrics	metric	NOUN
cana-1285	182	23	to	to	PART
cana-1285	182	24	evaluate	evaluate	VERB
cana-1285	182	25	it	it	PRON
cana-1285	182	26	.	.	PUNCT
cana-1285	183	1	greater	great	ADJ
cana-1285	183	2	variance	variance	NOUN
cana-1285	183	3	explained	explain	VERB
cana-1285	183	4	indicates	indicate	VERB
cana-1285	183	5	that	that	SCONJ
cana-1285	183	6	the	the	DET
cana-1285	183	7	model	model	NOUN
cana-1285	183	8	quite	quite	ADV
cana-1285	183	9	fairly	fairly	ADV
cana-1285	183	10	captures	capture	VERB
cana-1285	183	11	the	the	DET
cana-1285	183	12	variation	variation	NOUN
cana-1285	183	13	of	of	ADP
cana-1285	183	14	the	the	DET
cana-1285	183	15	data	datum	NOUN
cana-1285	183	16	.	.	PUNCT
cana-1285	184	1	2	2	NUM
cana-1285	184	2	sum	sum	NOUN
cana-1285	184	3	of	of	ADP
cana-1285	184	4	squares	square	NOUN
cana-1285	184	5	of	of	ADP
cana-1285	184	6	residuals	residual	NOUN
cana-1285	184	7	(	(	PUNCT
cana-1285	184	8	ssr	ssr	X
cana-1285	184	9	)	)	PUNCT
cana-1285	184	10	1	1	NUM
cana-1285	184	11	total	total	NOUN
cana-1285	184	12	sum	sum	NOUN
cana-1285	184	13	of	of	ADP
cana-1285	184	14	squares	square	NOUN
cana-1285	184	15	(	(	PUNCT
cana-1285	184	16	sst	sst	NOUN
cana-1285	184	17	)	)	PUNCT
cana-1285	184	18	r	r	NOUN
cana-1285	184	19	=	=	PUNCT
cana-1285	184	20	−	−	PROPN
cana-1285	184	21	the	the	DET
cana-1285	184	22	results	result	NOUN
cana-1285	184	23	of	of	ADP
cana-1285	184	24	figure	figure	NOUN
cana-1285	184	25	2–6	2–6	NUM
cana-1285	184	26	show	show	VERB
cana-1285	184	27	the	the	DET
cana-1285	184	28	proposed	propose	VERB
cana-1285	184	29	deep	deep	ADJ
cana-1285	184	30	learning	learning	NOUN
cana-1285	184	31	model	model	NOUN
cana-1285	184	32	,	,	PUNCT
cana-1285	184	33	which	which	PRON
cana-1285	184	34	integrates	integrate	VERB
cana-1285	184	35	nonlinear	nonlinear	ADJ
cana-1285	184	36	dynamics	dynamic	NOUN
cana-1285	184	37	,	,	PUNCT
cana-1285	184	38	against	against	ADP
cana-1285	184	39	current	current	ADJ
cana-1285	184	40	methods	method	NOUN
cana-1285	184	41	:	:	PUNCT
cana-1285	184	42	unidl4biopep	unidl4biopep	NOUN
cana-1285	184	43	,	,	PUNCT
cana-1285	184	44	neural	neural	ADJ
cana-1285	184	45	network	network	NOUN
cana-1285	184	46	language	language	NOUN
cana-1285	184	47	model	model	NOUN
cana-1285	184	48	(	(	PUNCT
cana-1285	184	49	nnlm	nnlm	PROPN
cana-1285	184	50	)	)	PUNCT
cana-1285	184	51	,	,	PUNCT
cana-1285	184	52	and	and	CCONJ
cana-1285	184	53	deepbio	deepbio	NOUN
cana-1285	184	54	.	.	PUNCT
cana-1285	185	1	figure	figure	NOUN
cana-1285	185	2	2	2	NUM
cana-1285	185	3	:	:	PUNCT
cana-1285	185	4	mean	mean	VERB
cana-1285	185	5	squared	square	VERB
cana-1285	185	6	error	error	NOUN
cana-1285	185	7	(	(	PUNCT
cana-1285	185	8	mse	mse	NOUN
cana-1285	185	9	)	)	PUNCT
cana-1285	185	10	the	the	DET
cana-1285	185	11	mean	mean	ADJ
cana-1285	185	12	squared	square	VERB
cana-1285	185	13	error	error	NOUN
cana-1285	185	14	(	(	PUNCT
cana-1285	185	15	mse	mse	NOUN
cana-1285	185	16	)	)	PUNCT
cana-1285	185	17	is	be	AUX
cana-1285	185	18	a	a	DET
cana-1285	185	19	basic	basic	ADJ
cana-1285	185	20	gauging	gauging	NOUN
cana-1285	185	21	of	of	ADP
cana-1285	185	22	prediction	prediction	NOUN
cana-1285	185	23	accuracy	accuracy	NOUN
cana-1285	185	24	.	.	PUNCT
cana-1285	186	1	under	under	ADP
cana-1285	186	2	the	the	DET
cana-1285	186	3	proposed	propose	VERB
cana-1285	186	4	model	model	NOUN
cana-1285	186	5	,	,	PUNCT
cana-1285	186	6	the	the	DET
cana-1285	186	7	mse	mse	NOUN
cana-1285	186	8	values	value	NOUN
cana-1285	186	9	vary	vary	VERB
cana-1285	186	10	progressively	progressively	ADV
cana-1285	186	11	from	from	ADP
cana-1285	186	12	0.054	0.054	NUM
cana-1285	186	13	at	at	ADP
cana-1285	186	14	25	25	NUM
cana-1285	186	15	epochs	epoch	NOUN
cana-1285	186	16	to	to	ADP
cana-1285	186	17	0.038	0.038	NUM
cana-1285	186	18	at	at	ADP
cana-1285	186	19	100	100	NUM
cana-1285	186	20	epochs	epoch	NOUN
cana-1285	186	21	.	.	PUNCT
cana-1285	187	1	this	this	DET
cana-1285	187	2	fall	fall	NOUN
cana-1285	187	3	reveals	reveal	VERB
cana-1285	187	4	the	the	DET
cana-1285	187	5	growing	grow	VERB
cana-1285	187	6	over	over	ADP
cana-1285	187	7	time	time	NOUN
cana-1285	187	8	accuracy	accuracy	NOUN
cana-1285	187	9	of	of	ADP
cana-1285	187	10	the	the	DET
cana-1285	187	11	model	model	NOUN
cana-1285	187	12	.	.	PUNCT
cana-1285	188	1	unidl4biopep	unidl4biopep	PRON
cana-1285	188	2	's	's	PART
cana-1285	188	3	mse	mse	NOUN
cana-1285	188	4	starts	start	VERB
cana-1285	188	5	at	at	ADP
cana-1285	188	6	0.079	0.079	NUM
cana-1285	188	7	and	and	CCONJ
cana-1285	188	8	works	work	VERB
cana-1285	188	9	down	down	ADV
cana-1285	188	10	to	to	ADP
cana-1285	188	11	0.065	0.065	NUM
cana-1285	188	12	.	.	PUNCT
cana-1285	189	1	nnlm	nnlm	PROPN
cana-1285	189	2	has	have	VERB
cana-1285	189	3	similar	similar	ADJ
cana-1285	189	4	mse	mse	NOUN
cana-1285	189	5	values	value	NOUN
cana-1285	189	6	starting	start	VERB
cana-1285	189	7	at	at	ADP
cana-1285	189	8	0.082	0.082	NUM
cana-1285	189	9	and	and	CCONJ
cana-1285	189	10	working	work	VERB
cana-1285	189	11	to	to	ADP
cana-1285	189	12	0.070	0.070	NUM
cana-1285	189	13	.	.	PUNCT
cana-1285	190	1	deepbio	deepbio	PROPN
cana-1285	190	2	demonstrates	demonstrate	VERB
cana-1285	190	3	better	well	ADJ
cana-1285	190	4	performance	performance	NOUN
cana-1285	190	5	than	than	ADP
cana-1285	190	6	unidl4biopep	unidl4biopep	PRON
cana-1285	190	7	and	and	CCONJ
cana-1285	190	8	nnlm	nnlm	VERB
cana-1285	190	9	with	with	ADP
cana-1285	190	10	mse	mse	NOUN
cana-1285	190	11	values	value	NOUN
cana-1285	190	12	from	from	ADP
cana-1285	190	13	0.070	0.070	NUM
cana-1285	190	14	at	at	ADP
cana-1285	190	15	25	25	NUM
cana-1285	190	16	epochs	epoch	NOUN
cana-1285	190	17	to	to	ADP
cana-1285	190	18	0.060	0.060	NUM
cana-1285	190	19	at	at	ADP
cana-1285	190	20	100	100	NUM
cana-1285	190	21	epochs	epoch	NOUN
cana-1285	190	22	.	.	PUNCT
cana-1285	191	1	reflecting	reflect	VERB
cana-1285	191	2	its	its	PRON
cana-1285	191	3	improved	improved	ADJ
cana-1285	191	4	prediction	prediction	NOUN
cana-1285	191	5	capability	capability	NOUN
cana-1285	191	6	,	,	PUNCT
cana-1285	191	7	the	the	DET
cana-1285	191	8	proposed	propose	VERB
cana-1285	191	9	model	model	NOUN
cana-1285	191	10	always	always	ADV
cana-1285	191	11	runs	run	VERB
cana-1285	191	12	with	with	ADP
cana-1285	191	13	the	the	DET
cana-1285	191	14	lowest	low	ADJ
cana-1285	191	15	mse	mse	NOUN
cana-1285	191	16	.	.	PUNCT
cana-1285	192	1	figure	figure	VERB
cana-1285	192	2	3	3	NUM
cana-1285	192	3	:	:	PUNCT
cana-1285	192	4	interpretability	interpretability	NOUN
cana-1285	192	5	interpretability	interpretability	NOUN
cana-1285	192	6	measures	measure	NOUN
cana-1285	192	7	,	,	PUNCT
cana-1285	192	8	in	in	ADP
cana-1285	192	9	the	the	DET
cana-1285	192	10	setting	setting	NOUN
cana-1285	192	11	of	of	ADP
cana-1285	192	12	the	the	DET
cana-1285	192	13	fundamental	fundamental	ADJ
cana-1285	192	14	dynamics	dynamic	NOUN
cana-1285	192	15	,	,	PUNCT
cana-1285	192	16	the	the	DET
cana-1285	192	17	clarity	clarity	NOUN
cana-1285	192	18	of	of	ADP
cana-1285	192	19	the	the	DET
cana-1285	192	20	model	model	NOUN
cana-1285	192	21	's	's	PART
cana-1285	192	22	projections	projection	NOUN
cana-1285	192	23	.	.	PUNCT
cana-1285	193	1	the	the	DET
cana-1285	193	2	proposed	propose	VERB
cana-1285	193	3	model	model	NOUN
cana-1285	193	4	rates	rate	NOUN
cana-1285	193	5	better	well	ADV
cana-1285	193	6	on	on	ADP
cana-1285	193	7	interpretability	interpretability	NOUN
cana-1285	193	8	than	than	ADP
cana-1285	193	9	existing	exist	VERB
cana-1285	193	10	methods	method	NOUN
cana-1285	193	11	when	when	SCONJ
cana-1285	193	12	values	value	NOUN
cana-1285	193	13	increase	increase	VERB
cana-1285	193	14	from	from	ADP
cana-1285	193	15	0.87	0.87	NUM
cana-1285	193	16	at	at	ADP
cana-1285	193	17	25	25	NUM
cana-1285	193	18	epochs	epoch	NOUN
cana-1285	193	19	to	to	ADP
cana-1285	193	20	0.92	0.92	NUM
cana-1285	193	21	at	at	ADP
cana-1285	193	22	100	100	NUM
cana-1285	193	23	epochs	epoch	NOUN
cana-1285	193	24	.	.	PUNCT
cana-1285	194	1	unidl4biopep	unidl4biopep	PROPN
cana-1285	194	2	starts	start	VERB
cana-1285	194	3	with	with	ADP
cana-1285	194	4	interpretability	interpretability	NOUN
cana-1285	194	5	of	of	ADP
cana-1285	194	6	0.80	0.80	NUM
cana-1285	194	7	and	and	CCONJ
cana-1285	194	8	rises	rise	VERB
cana-1285	194	9	to	to	ADP
cana-1285	194	10	0.85	0.85	NUM
cana-1285	194	11	.	.	PUNCT
cana-1285	195	1	nnlm	nnlm	PROPN
cana-1285	195	2	gets	get	VERB
cana-1285	195	3	lower	low	ADJ
cana-1285	195	4	marks	mark	NOUN
cana-1285	195	5	starting	start	VERB
cana-1285	195	6	with	with	ADP
cana-1285	195	7	0.78	0.78	NUM
cana-1285	195	8	and	and	CCONJ
cana-1285	195	9	working	work	VERB
cana-1285	195	10	up	up	ADV
cana-1285	195	11	to	to	ADP
cana-1285	195	12	0.82	0.82	NUM
cana-1285	195	13	.	.	PUNCT
cana-1285	196	1	deepbio	deepbio	PROPN
cana-1285	196	2	earns	earn	VERB
cana-1285	196	3	scores	score	NOUN
cana-1285	196	4	between	between	ADP
cana-1285	196	5	0.82	0.82	NUM
cana-1285	196	6	and	and	CCONJ
cana-1285	196	7	0.86	0.86	NUM
cana-1285	196	8	.	.	PUNCT
cana-1285	197	1	the	the	DET
cana-1285	197	2	greater	great	ADJ
cana-1285	197	3	interpretability	interpretability	NOUN
cana-1285	197	4	of	of	ADP
cana-1285	197	5	the	the	DET
cana-1285	197	6	proposed	propose	VERB
cana-1285	197	7	model	model	NOUN
cana-1285	197	8	suggests	suggest	VERB
cana-1285	197	9	that	that	SCONJ
cana-1285	197	10	it	it	PRON
cana-1285	197	11	communications	communication	VERB
cana-1285	197	12	on	on	ADP
cana-1285	197	13	applied	apply	VERB
cana-1285	197	14	nonlinear	nonlinear	ADJ
cana-1285	197	15	analysis	analysis	NOUN
cana-1285	197	16	issn	issn	NOUN
cana-1285	197	17	:	:	PUNCT
cana-1285	197	18	1074	1074	NUM
cana-1285	197	19	-	-	PUNCT
cana-1285	197	20	133x	133x	NUM
cana-1285	197	21	vol	vol	NOUN
cana-1285	197	22	31	31	NUM
cana-1285	197	23	no	no	NOUN
cana-1285	197	24	.	.	PUNCT
cana-1285	198	1	7s	7	NOUN
cana-1285	198	2	(	(	PUNCT
cana-1285	198	3	2024	2024	NUM
cana-1285	198	4	)	)	PUNCT
cana-1285	198	5	79	79	NUM
cana-1285	198	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1285	198	7	not	not	PART
cana-1285	198	8	only	only	ADV
cana-1285	198	9	provides	provide	VERB
cana-1285	198	10	accurate	accurate	ADJ
cana-1285	198	11	predictions	prediction	NOUN
cana-1285	198	12	but	but	CCONJ
cana-1285	198	13	also	also	ADV
cana-1285	198	14	fits	fit	VERB
cana-1285	198	15	very	very	ADV
cana-1285	198	16	neatly	neatly	ADV
cana-1285	198	17	with	with	ADP
cana-1285	198	18	the	the	DET
cana-1285	198	19	dynamic	dynamic	ADJ
cana-1285	198	20	system	system	NOUN
cana-1285	198	21	constraints	constraint	NOUN
cana-1285	198	22	,	,	PUNCT
cana-1285	198	23	hence	hence	ADV
cana-1285	198	24	boosting	boost	VERB
cana-1285	198	25	transparency	transparency	NOUN
cana-1285	198	26	and	and	CCONJ
cana-1285	198	27	dependability	dependability	NOUN
cana-1285	198	28	in	in	ADP
cana-1285	198	29	understanding	understand	VERB
cana-1285	198	30	biological	biological	ADJ
cana-1285	198	31	processes	process	NOUN
cana-1285	198	32	.	.	PUNCT
cana-1285	199	1	figure	figure	VERB
cana-1285	199	2	4	4	NUM
cana-1285	199	3	:	:	PUNCT
cana-1285	199	4	training	training	NOUN
cana-1285	199	5	time	time	NOUN
cana-1285	199	6	the	the	DET
cana-1285	199	7	evaluation	evaluation	NOUN
cana-1285	199	8	of	of	ADP
cana-1285	199	9	computing	compute	VERB
cana-1285	199	10	performance	performance	NOUN
cana-1285	199	11	mostly	mostly	ADV
cana-1285	199	12	relies	rely	VERB
cana-1285	199	13	on	on	ADP
cana-1285	199	14	the	the	DET
cana-1285	199	15	training	training	NOUN
cana-1285	199	16	length	length	NOUN
cana-1285	199	17	.	.	PUNCT
cana-1285	200	1	training	training	NOUN
cana-1285	200	2	times	time	NOUN
cana-1285	200	3	for	for	ADP
cana-1285	200	4	the	the	DET
cana-1285	200	5	proposed	propose	VERB
cana-1285	200	6	model	model	NOUN
cana-1285	200	7	go	go	VERB
cana-1285	200	8	from	from	ADP
cana-1285	200	9	1.2	1.2	NUM
cana-1285	200	10	hours	hour	NOUN
cana-1285	200	11	at	at	ADP
cana-1285	200	12	25	25	NUM
cana-1285	200	13	epochs	epoch	NOUN
cana-1285	200	14	to	to	ADP
cana-1285	200	15	4.7	4.7	NUM
cana-1285	200	16	hours	hour	NOUN
cana-1285	200	17	at	at	ADP
cana-1285	200	18	100	100	NUM
cana-1285	200	19	epochs	epoch	NOUN
cana-1285	200	20	.	.	PUNCT
cana-1285	201	1	with	with	ADP
cana-1285	201	2	running	run	VERB
cana-1285	201	3	1.5	1.5	NUM
cana-1285	201	4	hours	hour	NOUN
cana-1285	201	5	at	at	ADP
cana-1285	201	6	25	25	NUM
cana-1285	201	7	epochs	epoch	NOUN
cana-1285	201	8	and	and	CCONJ
cana-1285	201	9	spanning	span	VERB
cana-1285	201	10	6.0	6.0	NUM
cana-1285	201	11	hours	hour	NOUN
cana-1285	201	12	at	at	ADP
cana-1285	201	13	100	100	NUM
cana-1285	201	14	epochs	epoch	NOUN
cana-1285	201	15	,	,	PUNCT
cana-1285	201	16	the	the	DET
cana-1285	201	17	proposed	propose	VERB
cana-1285	201	18	model	model	NOUN
cana-1285	201	19	is	be	AUX
cana-1285	201	20	more	more	ADV
cana-1285	201	21	efficient	efficient	ADJ
cana-1285	201	22	than	than	ADP
cana-1285	201	23	unidl4biopep	unidl4biopep	PROPN
cana-1285	201	24	.	.	PUNCT
cana-1285	202	1	nnlm	nnlm	PROPN
cana-1285	202	2	asks	ask	VERB
cana-1285	202	3	for	for	ADP
cana-1285	202	4	more	more	ADJ
cana-1285	202	5	time	time	NOUN
cana-1285	202	6	,	,	PUNCT
cana-1285	202	7	starting	start	VERB
cana-1285	202	8	at	at	ADP
cana-1285	202	9	1.6	1.6	NUM
cana-1285	202	10	hours	hour	NOUN
cana-1285	202	11	and	and	CCONJ
cana-1285	202	12	working	work	VERB
cana-1285	202	13	till	till	SCONJ
cana-1285	202	14	6.2	6.2	NUM
cana-1285	202	15	hours	hour	NOUN
cana-1285	202	16	.	.	PUNCT
cana-1285	203	1	deepbio	deepbio	PROPN
cana-1285	203	2	is	be	AUX
cana-1285	203	3	also	also	ADV
cana-1285	203	4	competitive	competitive	ADJ
cana-1285	203	5	given	give	VERB
cana-1285	203	6	a	a	DET
cana-1285	203	7	1.4	1.4	NUM
cana-1285	203	8	to	to	PART
cana-1285	203	9	5.5	5.5	NUM
cana-1285	203	10	hour	hour	NOUN
cana-1285	203	11	training	training	NOUN
cana-1285	203	12	session	session	NOUN
cana-1285	203	13	.	.	PUNCT
cana-1285	204	1	though	though	SCONJ
cana-1285	204	2	at	at	ADP
cana-1285	204	3	higher	high	ADJ
cana-1285	204	4	epochs	epoch	NOUN
cana-1285	204	5	the	the	DET
cana-1285	204	6	recommended	recommend	VERB
cana-1285	204	7	model	model	NOUN
cana-1285	204	8	's	's	PART
cana-1285	204	9	training	training	NOUN
cana-1285	204	10	length	length	NOUN
cana-1285	204	11	is	be	AUX
cana-1285	204	12	significantly	significantly	ADV
cana-1285	204	13	more	more	ADJ
cana-1285	204	14	than	than	ADP
cana-1285	204	15	deepbio	deepbio	NOUN
cana-1285	204	16	,	,	PUNCT
cana-1285	204	17	it	it	PRON
cana-1285	204	18	displays	display	VERB
cana-1285	204	19	a	a	DET
cana-1285	204	20	balance	balance	NOUN
cana-1285	204	21	between	between	ADP
cana-1285	204	22	performance	performance	NOUN
cana-1285	204	23	and	and	CCONJ
cana-1285	204	24	efficiency	efficiency	NOUN
cana-1285	204	25	.	.	PUNCT
cana-1285	205	1	figure	figure	VERB
cana-1285	205	2	5	5	NUM
cana-1285	205	3	:	:	PUNCT
cana-1285	205	4	inference	inference	NOUN
cana-1285	205	5	speed	speed	NOUN
cana-1285	205	6	inferences	inference	VERB
cana-1285	205	7	speed	speed	NOUN
cana-1285	205	8	counts	count	VERB
cana-1285	205	9	the	the	DET
cana-1285	205	10	time	time	NOUN
cana-1285	205	11	required	require	VERB
cana-1285	205	12	to	to	PART
cana-1285	205	13	generate	generate	VERB
cana-1285	205	14	predictions	prediction	NOUN
cana-1285	205	15	once	once	SCONJ
cana-1285	205	16	the	the	DET
cana-1285	205	17	model	model	NOUN
cana-1285	205	18	is	be	AUX
cana-1285	205	19	trained	train	VERB
cana-1285	205	20	.	.	PUNCT
cana-1285	206	1	while	while	SCONJ
cana-1285	206	2	starting	start	VERB
cana-1285	206	3	at	at	ADP
cana-1285	206	4	15	15	NUM
cana-1285	206	5	milliseconds	millisecond	NOUN
cana-1285	206	6	and	and	CCONJ
cana-1285	206	7	rising	rise	VERB
cana-1285	206	8	to	to	ADP
cana-1285	206	9	12	12	NUM
cana-1285	206	10	milliseconds	millisecond	NOUN
cana-1285	206	11	,	,	PUNCT
cana-1285	206	12	the	the	DET
cana-1285	206	13	proposed	propose	VERB
cana-1285	206	14	model	model	NOUN
cana-1285	206	15	reduces	reduce	VERB
cana-1285	206	16	unidl4biopep	unidl4biopep	NOUN
cana-1285	206	17	by	by	ADP
cana-1285	206	18	exhibiting	exhibit	VERB
cana-1285	206	19	the	the	DET
cana-1285	206	20	fastest	fast	ADJ
cana-1285	206	21	inference	inference	NOUN
cana-1285	206	22	speed	speed	NOUN
cana-1285	206	23	—	—	PUNCT
cana-1285	206	24	from	from	ADP
cana-1285	206	25	12	12	NUM
cana-1285	206	26	milliseconds	millisecond	NOUN
cana-1285	206	27	per	per	ADP
cana-1285	206	28	sample	sample	NOUN
cana-1285	206	29	at	at	ADP
cana-1285	206	30	25	25	NUM
cana-1285	206	31	epochs	epoch	NOUN
cana-1285	206	32	to	to	ADP
cana-1285	206	33	8	8	NUM
cana-1285	206	34	milliseconds	millisecond	NOUN
cana-1285	206	35	at	at	ADP
cana-1285	206	36	100	100	NUM
cana-1285	206	37	epochs	epoch	NOUN
cana-1285	206	38	.	.	PUNCT
cana-1285	207	1	nnlm	nnlm	PROPN
cana-1285	207	2	is	be	AUX
cana-1285	207	3	slower	slow	ADJ
cana-1285	207	4	ranging	range	VERB
cana-1285	207	5	from	from	ADP
cana-1285	207	6	16	16	NUM
cana-1285	207	7	milliseconds	millisecond	NOUN
cana-1285	207	8	to	to	ADP
cana-1285	207	9	13	13	NUM
cana-1285	207	10	milliseconds	millisecond	NOUN
cana-1285	207	11	.	.	PUNCT
cana-1285	208	1	deepbio	deepbio	PROPN
cana-1285	208	2	shows	show	VERB
cana-1285	208	3	rates	rate	NOUN
cana-1285	208	4	of	of	ADP
cana-1285	208	5	14	14	NUM
cana-1285	208	6	milliseconds	millisecond	NOUN
cana-1285	208	7	to	to	ADP
cana-1285	208	8	11	11	NUM
cana-1285	208	9	millisecond	millisecond	NOUN
cana-1285	208	10	interval	interval	NOUN
cana-1285	208	11	.	.	PUNCT
cana-1285	209	1	the	the	DET
cana-1285	209	2	lower	low	ADJ
cana-1285	209	3	inference	inference	NOUN
cana-1285	209	4	time	time	NOUN
cana-1285	209	5	of	of	ADP
cana-1285	209	6	the	the	DET
cana-1285	209	7	proposed	propose	VERB
cana-1285	209	8	model	model	NOUN
cana-1285	209	9	implies	imply	VERB
cana-1285	209	10	its	its	PRON
cana-1285	209	11	appropriateness	appropriateness	NOUN
cana-1285	209	12	for	for	ADP
cana-1285	209	13	real	real	ADJ
cana-1285	209	14	-	-	PUNCT
cana-1285	209	15	time	time	NOUN
cana-1285	209	16	applications	application	NOUN
cana-1285	209	17	,	,	PUNCT
cana-1285	209	18	in	in	ADP
cana-1285	209	19	which	which	PRON
cana-1285	209	20	fast	fast	ADJ
cana-1285	209	21	predictions	prediction	NOUN
cana-1285	209	22	are	be	AUX
cana-1285	209	23	absolutely	absolutely	ADV
cana-1285	209	24	necessary	necessary	ADJ
cana-1285	209	25	.	.	PUNCT
cana-1285	210	1	communications	communication	NOUN
cana-1285	210	2	on	on	ADP
cana-1285	210	3	applied	apply	VERB
cana-1285	210	4	nonlinear	nonlinear	ADJ
cana-1285	210	5	analysis	analysis	NOUN
cana-1285	210	6	issn	issn	NOUN
cana-1285	210	7	:	:	PUNCT
cana-1285	210	8	1074	1074	NUM
cana-1285	210	9	-	-	PUNCT
cana-1285	210	10	133x	133x	NUM
cana-1285	210	11	vol	vol	NOUN
cana-1285	210	12	31	31	NUM
cana-1285	210	13	no	no	NOUN
cana-1285	210	14	.	.	PUNCT
cana-1285	211	1	7s	7	NOUN
cana-1285	211	2	(	(	PUNCT
cana-1285	211	3	2024	2024	NUM
cana-1285	211	4	)	)	PUNCT
cana-1285	211	5	80	80	NUM
cana-1285	211	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1285	211	7	figure	figure	NOUN
cana-1285	211	8	6	6	NUM
cana-1285	211	9	:	:	PUNCT
cana-1285	211	10	variance	variance	NOUN
cana-1285	211	11	explained	explain	VERB
cana-1285	211	12	(	(	PUNCT
cana-1285	211	13	r²	r²	NOUN
cana-1285	211	14	)	)	PUNCT
cana-1285	211	15	variance	variance	NOUN
cana-1285	211	16	explained	explain	VERB
cana-1285	211	17	,	,	PUNCT
cana-1285	211	18	r²	r²	VERB
cana-1285	211	19	,	,	PUNCT
cana-1285	211	20	indicates	indicate	VERB
cana-1285	211	21	the	the	DET
cana-1285	211	22	extent	extent	NOUN
cana-1285	211	23	of	of	ADP
cana-1285	211	24	data	datum	NOUN
cana-1285	211	25	variability	variability	NOUN
cana-1285	211	26	the	the	DET
cana-1285	211	27	model	model	NOUN
cana-1285	211	28	can	can	AUX
cana-1285	211	29	detect	detect	VERB
cana-1285	211	30	.	.	PUNCT
cana-1285	212	1	the	the	DET
cana-1285	212	2	recommended	recommend	VERB
cana-1285	212	3	model	model	NOUN
cana-1285	212	4	's	's	PART
cana-1285	212	5	r²	r²	NOUN
cana-1285	212	6	values	value	NOUN
cana-1285	212	7	indicate	indicate	VERB
cana-1285	212	8	improvement	improvement	NOUN
cana-1285	212	9	from	from	ADP
cana-1285	212	10	0.74	0.74	NUM
cana-1285	212	11	at	at	ADP
cana-1285	212	12	25	25	NUM
cana-1285	212	13	epochs	epoch	NOUN
cana-1285	212	14	to	to	ADP
cana-1285	212	15	0.82	0.82	NUM
cana-1285	212	16	at	at	ADP
cana-1285	212	17	100	100	NUM
cana-1285	212	18	epochs	epoch	NOUN
cana-1285	212	19	,	,	PUNCT
cana-1285	212	20	so	so	ADV
cana-1285	212	21	suggesting	suggest	VERB
cana-1285	212	22	its	its	PRON
cana-1285	212	23	efficiency	efficiency	NOUN
cana-1285	212	24	in	in	ADP
cana-1285	212	25	characterizing	characterize	VERB
cana-1285	212	26	data	datum	NOUN
cana-1285	212	27	variability	variability	NOUN
cana-1285	212	28	.	.	PUNCT
cana-1285	213	1	unidl4biopep	unidl4biopep	PROPN
cana-1285	213	2	starts	start	VERB
cana-1285	213	3	with	with	ADP
cana-1285	213	4	a	a	DET
cana-1285	213	5	r²	r²	NOUN
cana-1285	213	6	of	of	ADP
cana-1285	213	7	0.68	0.68	NUM
cana-1285	213	8	and	and	CCONJ
cana-1285	213	9	rises	rise	VERB
cana-1285	213	10	up	up	ADV
cana-1285	213	11	to	to	ADP
cana-1285	213	12	0.75	0.75	NUM
cana-1285	213	13	;	;	PUNCT
cana-1285	213	14	nnlm	nnlm	PROPN
cana-1285	213	15	's	's	PART
cana-1285	213	16	values	value	NOUN
cana-1285	213	17	run	run	VERB
cana-1285	213	18	0.65	0.65	NUM
cana-1285	213	19	to	to	ADP
cana-1285	213	20	0.72	0.72	NUM
cana-1285	213	21	.	.	PUNCT
cana-1285	214	1	deepbio	deepbio	PROPN
cana-1285	214	2	shows	show	VERB
cana-1285	214	3	range	range	NOUN
cana-1285	214	4	of	of	ADP
cana-1285	214	5	0.70	0.70	NUM
cana-1285	214	6	to	to	PART
cana-1285	214	7	0.76	0.76	NUM
cana-1285	214	8	.	.	PUNCT
cana-1285	215	1	higher	high	ADJ
cana-1285	215	2	r²	r²	NOUN
cana-1285	215	3	values	value	NOUN
cana-1285	215	4	of	of	ADP
cana-1285	215	5	the	the	DET
cana-1285	215	6	proposed	propose	VERB
cana-1285	215	7	model	model	NOUN
cana-1285	215	8	indicate	indicate	VERB
cana-1285	215	9	its	its	PRON
cana-1285	215	10	improved	improved	ADJ
cana-1285	215	11	potential	potential	NOUN
cana-1285	215	12	to	to	ADP
cana-1285	215	13	exactly	exactly	ADV
cana-1285	215	14	depict	depict	VERB
cana-1285	215	15	and	and	CCONJ
cana-1285	215	16	grasp	grasp	VERB
cana-1285	215	17	biological	biological	ADJ
cana-1285	215	18	facts	fact	NOUN
cana-1285	215	19	.	.	PUNCT
cana-1285	216	1	10	10	NUM
cana-1285	216	2	.	.	X
cana-1285	216	3	conclusion	conclusion	NOUN
cana-1285	216	4	comparatively	comparatively	ADV
cana-1285	216	5	to	to	ADP
cana-1285	216	6	current	current	ADJ
cana-1285	216	7	methods	method	NOUN
cana-1285	216	8	—	—	PUNCT
cana-1285	216	9	unidl4biopep	unidl4biopep	NOUN
cana-1285	216	10	,	,	PUNCT
cana-1285	216	11	neural	neural	ADJ
cana-1285	216	12	network	network	NOUN
cana-1285	216	13	language	language	NOUN
cana-1285	216	14	model	model	NOUN
cana-1285	216	15	(	(	PUNCT
cana-1285	216	16	nnlm	nnlm	PROPN
cana-1285	216	17	)	)	PUNCT
cana-1285	216	18	,	,	PUNCT
cana-1285	216	19	and	and	CCONJ
cana-1285	216	20	deepbio	deepbio	NOUN
cana-1285	216	21	—	—	PUNCT
cana-1285	216	22	the	the	DET
cana-1285	216	23	proposed	propose	VERB
cana-1285	216	24	deep	deep	ADJ
cana-1285	216	25	learning	learning	NOUN
cana-1285	216	26	model	model	NOUN
cana-1285	216	27	,	,	PUNCT
cana-1285	216	28	which	which	PRON
cana-1285	216	29	includes	include	VERB
cana-1285	216	30	nonlinear	nonlinear	ADJ
cana-1285	216	31	dynamics	dynamic	NOUN
cana-1285	216	32	,	,	PUNCT
cana-1285	216	33	shows	show	VERB
cana-1285	216	34	notable	notable	ADJ
cana-1285	216	35	performance	performance	NOUN
cana-1285	216	36	gains	gain	NOUN
cana-1285	216	37	across	across	ADP
cana-1285	216	38	numerous	numerous	ADJ
cana-1285	216	39	crucial	crucial	ADJ
cana-1285	216	40	criteria	criterion	NOUN
cana-1285	216	41	.	.	PUNCT
cana-1285	217	1	this	this	DET
cana-1285	217	2	highlights	highlight	VERB
cana-1285	217	3	its	its	PRON
cana-1285	217	4	quite	quite	ADV
cana-1285	217	5	precise	precise	ADJ
cana-1285	217	6	prediction	prediction	NOUN
cana-1285	217	7	of	of	ADP
cana-1285	217	8	metabolic	metabolic	NOUN
cana-1285	217	9	components	component	NOUN
cana-1285	217	10	.	.	PUNCT
cana-1285	218	1	in	in	ADP
cana-1285	218	2	terms	term	NOUN
cana-1285	218	3	of	of	ADP
cana-1285	218	4	interpretability	interpretability	NOUN
cana-1285	218	5	,	,	PUNCT
cana-1285	218	6	the	the	DET
cana-1285	218	7	proposed	propose	VERB
cana-1285	218	8	model	model	NOUN
cana-1285	218	9	also	also	ADV
cana-1285	218	10	shows	show	VERB
cana-1285	218	11	the	the	DET
cana-1285	218	12	best	good	ADJ
cana-1285	218	13	values	value	NOUN
cana-1285	218	14	indicating	indicate	VERB
cana-1285	218	15	better	well	ADJ
cana-1285	218	16	alignment	alignment	NOUN
cana-1285	218	17	with	with	ADP
cana-1285	218	18	the	the	DET
cana-1285	218	19	underlying	underlie	VERB
cana-1285	218	20	dynamic	dynamic	ADJ
cana-1285	218	21	system	system	NOUN
cana-1285	218	22	constraints	constraint	NOUN
cana-1285	218	23	.	.	PUNCT
cana-1285	219	1	for	for	ADP
cana-1285	219	2	uses	use	NOUN
cana-1285	219	3	requiring	require	VERB
cana-1285	219	4	interpretability	interpretability	NOUN
cana-1285	219	5	,	,	PUNCT
cana-1285	219	6	this	this	PRON
cana-1285	219	7	makes	make	VERB
cana-1285	219	8	the	the	DET
cana-1285	219	9	model	model	NOUN
cana-1285	219	10	more	more	ADV
cana-1285	219	11	visible	visible	ADJ
cana-1285	219	12	and	and	CCONJ
cana-1285	219	13	reliable	reliable	ADJ
cana-1285	219	14	for	for	ADP
cana-1285	219	15	understanding	understand	VERB
cana-1285	219	16	biological	biological	ADJ
cana-1285	219	17	processes	process	NOUN
cana-1285	219	18	.	.	PUNCT
cana-1285	220	1	training	training	NOUN
cana-1285	220	2	time	time	NOUN
cana-1285	220	3	is	be	AUX
cana-1285	220	4	well	well	ADV
cana-1285	220	5	-	-	PUNCT
cana-1285	220	6	regulated	regulate	VERB
cana-1285	220	7	with	with	ADP
cana-1285	220	8	respect	respect	NOUN
cana-1285	220	9	to	to	ADP
cana-1285	220	10	the	the	DET
cana-1285	220	11	complexity	complexity	NOUN
cana-1285	220	12	of	of	ADP
cana-1285	220	13	the	the	DET
cana-1285	220	14	model	model	NOUN
cana-1285	220	15	and	and	CCONJ
cana-1285	220	16	performance	performance	NOUN
cana-1285	220	17	,	,	PUNCT
cana-1285	220	18	even	even	ADV
cana-1285	220	19	if	if	SCONJ
cana-1285	220	20	it	it	PRON
cana-1285	220	21	is	be	AUX
cana-1285	220	22	somewhat	somewhat	ADV
cana-1285	220	23	greater	great	ADJ
cana-1285	220	24	than	than	ADP
cana-1285	220	25	some	some	DET
cana-1285	220	26	methods	method	NOUN
cana-1285	220	27	.	.	PUNCT
cana-1285	221	1	the	the	DET
cana-1285	221	2	proposed	propose	VERB
cana-1285	221	3	model	model	NOUN
cana-1285	221	4	's	's	PART
cana-1285	221	5	inference	inference	NOUN
cana-1285	221	6	performance	performance	NOUN
cana-1285	221	7	is	be	AUX
cana-1285	221	8	significantly	significantly	ADV
cana-1285	221	9	faster	fast	ADV
cana-1285	221	10	and	and	CCONJ
cana-1285	221	11	is	be	AUX
cana-1285	221	12	thus	thus	ADV
cana-1285	221	13	well	well	ADV
cana-1285	221	14	suitable	suitable	ADJ
cana-1285	221	15	for	for	ADP
cana-1285	221	16	real	real	ADJ
cana-1285	221	17	-	-	PUNCT
cana-1285	221	18	time	time	NOUN
cana-1285	221	19	applications	application	NOUN
cana-1285	221	20	where	where	SCONJ
cana-1285	221	21	fast	fast	ADJ
cana-1285	221	22	prediction	prediction	NOUN
cana-1285	221	23	is	be	AUX
cana-1285	221	24	absolutely	absolutely	ADV
cana-1285	221	25	important	important	ADJ
cana-1285	221	26	with	with	ADP
cana-1285	221	27	a	a	DET
cana-1285	221	28	drop	drop	NOUN
cana-1285	221	29	from	from	ADP
cana-1285	221	30	12	12	NUM
cana-1285	221	31	milliseconds	millisecond	NOUN
cana-1285	221	32	to	to	ADP
cana-1285	221	33	8	8	NUM
cana-1285	221	34	milliseconds	millisecond	NOUN
cana-1285	221	35	per	per	ADP
cana-1285	221	36	sample	sample	NOUN
cana-1285	221	37	.	.	PUNCT
cana-1285	222	1	computational	computational	ADJ
cana-1285	222	2	economy	economy	NOUN
cana-1285	222	3	,	,	PUNCT
cana-1285	222	4	which	which	PRON
cana-1285	222	5	maintains	maintain	VERB
cana-1285	222	6	high	high	ADJ
cana-1285	222	7	performance	performance	NOUN
cana-1285	222	8	even	even	ADV
cana-1285	222	9	in	in	ADP
cana-1285	222	10	more	more	ADJ
cana-1285	222	11	complex	complex	ADJ
cana-1285	222	12	models	model	NOUN
cana-1285	222	13	,	,	PUNCT
cana-1285	222	14	is	be	AUX
cana-1285	222	15	another	another	DET
cana-1285	222	16	big	big	ADJ
cana-1285	222	17	advantage	advantage	NOUN
cana-1285	222	18	of	of	ADP
cana-1285	222	19	the	the	DET
cana-1285	222	20	proposed	propose	VERB
cana-1285	222	21	model	model	NOUN
cana-1285	222	22	.	.	PUNCT
cana-1285	223	1	it	it	PRON
cana-1285	223	2	maximizes	maximize	VERB
cana-1285	223	3	training	training	NOUN
cana-1285	223	4	and	and	CCONJ
cana-1285	223	5	inference	inference	NOUN
cana-1285	223	6	processes	process	NOUN
cana-1285	223	7	by	by	ADP
cana-1285	223	8	balancing	balance	VERB
cana-1285	223	9	the	the	DET
cana-1285	223	10	utilization	utilization	NOUN
cana-1285	223	11	of	of	ADP
cana-1285	223	12	resources	resource	NOUN
cana-1285	223	13	.	.	PUNCT
cana-1285	224	1	among	among	ADP
cana-1285	224	2	the	the	DET
cana-1285	224	3	numerous	numerous	ADJ
cana-1285	224	4	methods	method	NOUN
cana-1285	224	5	,	,	PUNCT
cana-1285	224	6	the	the	DET
cana-1285	224	7	proposed	propose	VERB
cana-1285	224	8	model	model	NOUN
cana-1285	224	9	's	's	PART
cana-1285	224	10	variance	variance	NOUN
cana-1285	224	11	explained	explain	VERB
cana-1285	224	12	(	(	PUNCT
cana-1285	224	13	r²	r²	PROPN
cana-1285	224	14	)	)	PUNCT
cana-1285	224	15	is	be	AUX
cana-1285	224	16	the	the	DET
cana-1285	224	17	highest	high	ADJ
cana-1285	224	18	,	,	PUNCT
cana-1285	224	19	thereby	thereby	ADV
cana-1285	224	20	stressing	stress	VERB
cana-1285	224	21	its	its	PRON
cana-1285	224	22	ability	ability	NOUN
cana-1285	224	23	to	to	PART
cana-1285	224	24	sufficiently	sufficiently	ADV
cana-1285	224	25	reflect	reflect	VERB
cana-1285	224	26	and	and	CCONJ
cana-1285	224	27	explain	explain	VERB
cana-1285	224	28	a	a	DET
cana-1285	224	29	significant	significant	ADJ
cana-1285	224	30	percentage	percentage	NOUN
cana-1285	224	31	of	of	ADP
cana-1285	224	32	the	the	DET
cana-1285	224	33	variability	variability	NOUN
cana-1285	224	34	in	in	ADP
cana-1285	224	35	biochemical	biochemical	ADJ
cana-1285	224	36	data	datum	NOUN
cana-1285	224	37	.	.	PUNCT
cana-1285	225	1	this	this	PRON
cana-1285	225	2	indicates	indicate	VERB
cana-1285	225	3	that	that	SCONJ
cana-1285	225	4	the	the	DET
cana-1285	225	5	model	model	NOUN
cana-1285	225	6	not	not	PART
cana-1285	225	7	only	only	ADV
cana-1285	225	8	produces	produce	VERB
cana-1285	225	9	correct	correct	ADJ
cana-1285	225	10	forecasts	forecast	NOUN
cana-1285	225	11	but	but	CCONJ
cana-1285	225	12	also	also	ADV
cana-1285	225	13	a	a	DET
cana-1285	225	14	whole	whole	ADJ
cana-1285	225	15	awareness	awareness	NOUN
cana-1285	225	16	of	of	ADP
cana-1285	225	17	the	the	DET
cana-1285	225	18	data	data	NOUN
cana-1285	225	19	dynamics	dynamic	NOUN
cana-1285	225	20	.	.	PUNCT
cana-1285	226	1	references	reference	NOUN
cana-1285	226	2	[	[	X
cana-1285	226	3	1	1	NUM
cana-1285	226	4	]	]	X
cana-1285	226	5	partin	partin	X
cana-1285	226	6	,	,	PUNCT
cana-1285	226	7	a.	a.	NOUN
cana-1285	226	8	,	,	PUNCT
cana-1285	226	9	brettin	brettin	ADJ
cana-1285	226	10	,	,	PUNCT
cana-1285	226	11	t.	t.	PROPN
cana-1285	226	12	s.	s.	PROPN
cana-1285	226	13	,	,	PUNCT
cana-1285	226	14	zhu	zhu	PROPN
cana-1285	226	15	,	,	PUNCT
cana-1285	226	16	y.	y.	PROPN
cana-1285	226	17	,	,	PUNCT
cana-1285	226	18	narykov	narykov	PROPN
cana-1285	226	19	,	,	PUNCT
cana-1285	226	20	o.	o.	PROPN
cana-1285	226	21	,	,	PUNCT
cana-1285	226	22	clyde	clyde	PROPN
cana-1285	226	23	,	,	PUNCT
cana-1285	226	24	a.	a.	PROPN
cana-1285	226	25	,	,	PUNCT
cana-1285	226	26	overbeek	overbeek	NOUN
cana-1285	226	27	,	,	PUNCT
cana-1285	226	28	j.	j.	PROPN
cana-1285	226	29	,	,	PUNCT
cana-1285	226	30	&	&	CCONJ
cana-1285	226	31	stevens	stevens	PROPN
cana-1285	226	32	,	,	PUNCT
cana-1285	226	33	r.	r.	PROPN
cana-1285	226	34	l.	l.	PROPN
cana-1285	226	35	(	(	PUNCT
cana-1285	226	36	2023	2023	NUM
cana-1285	226	37	)	)	PUNCT
cana-1285	226	38	.	.	PUNCT
cana-1285	227	1	deep	deep	ADJ
cana-1285	227	2	learning	learning	NOUN
cana-1285	227	3	methods	method	NOUN
cana-1285	227	4	for	for	ADP
cana-1285	227	5	drug	drug	NOUN
cana-1285	227	6	response	response	NOUN
cana-1285	227	7	prediction	prediction	NOUN
cana-1285	227	8	in	in	ADP
cana-1285	227	9	cancer	cancer	NOUN
cana-1285	227	10	:	:	PUNCT
cana-1285	227	11	predominant	predominant	ADJ
cana-1285	227	12	and	and	CCONJ
cana-1285	227	13	emerging	emerge	VERB
cana-1285	227	14	trends	trend	NOUN
cana-1285	227	15	.	.	PUNCT
cana-1285	228	1	frontiers	frontier	NOUN
cana-1285	228	2	in	in	ADP
cana-1285	228	3	medicine	medicine	NOUN
cana-1285	228	4	,	,	PUNCT
cana-1285	228	5	10	10	NUM
cana-1285	228	6	,	,	PUNCT
cana-1285	228	7	1086097	1086097	NUM
cana-1285	228	8	.	.	PUNCT
cana-1285	229	1	[	[	X
cana-1285	229	2	2	2	NUM
cana-1285	229	3	]	]	SYM
cana-1285	229	4	nijaguna	nijaguna	PROPN
cana-1285	229	5	,	,	PUNCT
cana-1285	229	6	g.	g.	PROPN
cana-1285	229	7	s.	s.	PROPN
cana-1285	229	8	,	,	PUNCT
cana-1285	229	9	manjunath	manjunath	PROPN
cana-1285	229	10	,	,	PUNCT
cana-1285	229	11	d.	d.	PROPN
cana-1285	229	12	r.	r.	PROPN
cana-1285	229	13	,	,	PUNCT
cana-1285	229	14	abouhawwash	abouhawwash	NOUN
cana-1285	229	15	,	,	PUNCT
cana-1285	229	16	m.	m.	NOUN
cana-1285	229	17	,	,	PUNCT
cana-1285	229	18	askar	askar	PROPN
cana-1285	229	19	,	,	PUNCT
cana-1285	229	20	s.	s.	PROPN
cana-1285	229	21	s.	s.	PROPN
cana-1285	229	22	,	,	PUNCT
cana-1285	229	23	basha	basha	PROPN
cana-1285	229	24	,	,	PUNCT
cana-1285	229	25	d.	d.	PROPN
cana-1285	229	26	k.	k.	PROPN
cana-1285	229	27	,	,	PUNCT
cana-1285	229	28	&	&	CCONJ
cana-1285	229	29	sengupta	sengupta	PROPN
cana-1285	229	30	,	,	PUNCT
cana-1285	229	31	j.	j.	PROPN
cana-1285	229	32	(	(	PUNCT
cana-1285	229	33	2023	2023	NUM
cana-1285	229	34	)	)	PUNCT
cana-1285	229	35	.	.	PUNCT
cana-1285	230	1	deep	deep	ADJ
cana-1285	230	2	learning	learning	NOUN
cana-1285	230	3	-	-	PUNCT
cana-1285	230	4	based	base	VERB
cana-1285	230	5	improved	improved	ADJ
cana-1285	230	6	wcm	wcm	NOUN
cana-1285	230	7	technique	technique	NOUN
cana-1285	230	8	for	for	ADP
cana-1285	230	9	soil	soil	NOUN
cana-1285	230	10	moisture	moisture	NOUN
cana-1285	230	11	retrieval	retrieval	NOUN
cana-1285	230	12	with	with	ADP
cana-1285	230	13	satellite	satellite	NOUN
cana-1285	230	14	images	image	NOUN
cana-1285	230	15	.	.	PUNCT
cana-1285	231	1	remote	remote	ADJ
cana-1285	231	2	sensing	sensing	NOUN
cana-1285	231	3	,	,	PUNCT
cana-1285	231	4	15(8	15(8	NOUN
cana-1285	231	5	)	)	PUNCT
cana-1285	231	6	,	,	PUNCT
cana-1285	231	7	2005	2005	NUM
cana-1285	232	1	[	[	X
cana-1285	232	2	3	3	NUM
cana-1285	232	3	]	]	X
cana-1285	232	4	tissaoui	tissaoui	PROPN
cana-1285	232	5	,	,	PUNCT
cana-1285	232	6	k.	k.	PROPN
cana-1285	232	7	,	,	PUNCT
cana-1285	232	8	zaghdoudi	zaghdoudi	NUM
cana-1285	232	9	,	,	PUNCT
cana-1285	232	10	t.	t.	PROPN
cana-1285	232	11	,	,	PUNCT
cana-1285	232	12	boubaker	boubaker	PROPN
cana-1285	232	13	,	,	PUNCT
cana-1285	232	14	s.	s.	PROPN
cana-1285	232	15	,	,	PUNCT
cana-1285	232	16	hkiri	hkiri	PROPN
cana-1285	232	17	,	,	PUNCT
cana-1285	232	18	b.	b.	PROPN
cana-1285	232	19	,	,	PUNCT
cana-1285	232	20	&	&	CCONJ
cana-1285	232	21	talbi	talbi	PROPN
cana-1285	232	22	,	,	PUNCT
cana-1285	232	23	m.	m.	NOUN
cana-1285	232	24	(	(	PUNCT
cana-1285	232	25	2024	2024	NUM
cana-1285	232	26	)	)	PUNCT
cana-1285	232	27	.	.	PUNCT
cana-1285	233	1	testing	test	VERB
cana-1285	233	2	the	the	DET
cana-1285	233	3	nonlinear	nonlinear	ADJ
cana-1285	233	4	long	long	ADJ
cana-1285	233	5	-	-	PUNCT
cana-1285	233	6	and	and	CCONJ
cana-1285	233	7	short	short	ADJ
cana-1285	233	8	-	-	PUNCT
cana-1285	233	9	run	run	VERB
cana-1285	233	10	distributional	distributional	ADJ
cana-1285	233	11	asymmetries	asymmetry	NOUN
cana-1285	233	12	effects	effect	NOUN
cana-1285	233	13	of	of	ADP
cana-1285	233	14	bitcoin	bitcoin	ADJ
cana-1285	233	15	prices	price	NOUN
cana-1285	233	16	on	on	ADP
cana-1285	233	17	bitcoin	bitcoin	ADJ
cana-1285	233	18	energy	energy	NOUN
cana-1285	233	19	consumption	consumption	NOUN
cana-1285	233	20	:	:	PUNCT
cana-1285	233	21	new	new	ADJ
cana-1285	233	22	insights	insight	NOUN
cana-1285	233	23	through	through	ADP
cana-1285	233	24	the	the	DET
cana-1285	233	25	qnardl	qnardl	NOUN
cana-1285	233	26	model	model	NOUN
cana-1285	233	27	and	and	CCONJ
cana-1285	233	28	xgboost	xgboost	ADV
cana-1285	233	29	machine	machine	NOUN
cana-1285	233	30	-	-	PUNCT
cana-1285	233	31	learning	learn	VERB
cana-1285	233	32	tool	tool	NOUN
cana-1285	233	33	.	.	PUNCT
cana-1285	234	1	energies	energy	NOUN
cana-1285	234	2	,	,	PUNCT
cana-1285	234	3	17(12	17(12	NUM
cana-1285	234	4	)	)	PUNCT
cana-1285	234	5	,	,	PUNCT
cana-1285	234	6	2810	2810	NUM
cana-1285	234	7	.	.	PUNCT
cana-1285	235	1	[	[	X
cana-1285	235	2	4	4	X
cana-1285	235	3	]	]	X
cana-1285	235	4	ziani	ziani	X
cana-1285	235	5	,	,	PUNCT
cana-1285	235	6	s.	s.	PROPN
cana-1285	235	7	(	(	PUNCT
cana-1285	235	8	2024	2024	NUM
cana-1285	235	9	)	)	PUNCT
cana-1285	235	10	.	.	PUNCT
cana-1285	236	1	enhancing	enhance	VERB
cana-1285	236	2	fetal	fetal	ADJ
cana-1285	236	3	electrocardiogram	electrocardiogram	NOUN
cana-1285	236	4	classification	classification	NOUN
cana-1285	236	5	:	:	PUNCT
cana-1285	236	6	a	a	DET
cana-1285	236	7	hybrid	hybrid	ADJ
cana-1285	236	8	approach	approach	NOUN
cana-1285	236	9	incorporating	incorporate	VERB
cana-1285	236	10	multimodal	multimodal	NOUN
cana-1285	236	11	data	datum	NOUN
cana-1285	236	12	fusion	fusion	NOUN
cana-1285	236	13	and	and	CCONJ
cana-1285	236	14	advanced	advanced	ADJ
cana-1285	236	15	deep	deep	ADJ
cana-1285	236	16	learning	learning	NOUN
cana-1285	236	17	models	model	NOUN
cana-1285	236	18	.	.	PUNCT
cana-1285	237	1	multimedia	multimedia	NOUN
cana-1285	237	2	tools	tool	NOUN
cana-1285	237	3	and	and	CCONJ
cana-1285	237	4	applications	application	NOUN
cana-1285	237	5	,	,	PUNCT
cana-1285	237	6	83(18	83(18	NUM
cana-1285	237	7	)	)	PUNCT
cana-1285	237	8	,	,	PUNCT
cana-1285	237	9	55011	55011	NUM
cana-1285	237	10	-	-	SYM
cana-1285	237	11	55051	55051	NUM
cana-1285	237	12	.	.	PUNCT
cana-1285	238	1	[	[	X
cana-1285	238	2	5	5	NUM
cana-1285	238	3	]	]	X
cana-1285	238	4	shaikh	shaikh	PROPN
cana-1285	238	5	,	,	PUNCT
cana-1285	238	6	z.	z.	PROPN
cana-1285	238	7	a.	a.	PROPN
cana-1285	238	8	,	,	PUNCT
cana-1285	238	9	khan	khan	PROPN
cana-1285	238	10	,	,	PUNCT
cana-1285	238	11	a.	a.	NOUN
cana-1285	238	12	a.	a.	PROPN
cana-1285	238	13	,	,	PUNCT
cana-1285	238	14	baitenova	baitenova	PROPN
cana-1285	238	15	,	,	PUNCT
cana-1285	238	16	l.	l.	PROPN
cana-1285	238	17	,	,	PUNCT
cana-1285	238	18	zambinova	zambinova	PROPN
cana-1285	238	19	,	,	PUNCT
cana-1285	238	20	g.	g.	PROPN
cana-1285	238	21	,	,	PUNCT
cana-1285	238	22	yegina	yegina	PROPN
cana-1285	238	23	,	,	PUNCT
cana-1285	238	24	n.	n.	NOUN
cana-1285	238	25	,	,	PUNCT
cana-1285	238	26	ivolgina	ivolgina	PROPN
cana-1285	238	27	,	,	PUNCT
cana-1285	238	28	n.	n.	NOUN
cana-1285	238	29	,	,	PUNCT
cana-1285	238	30	...	...	PUNCT
cana-1285	238	31	&	&	CCONJ
cana-1285	238	32	barykin	barykin	PROPN
cana-1285	238	33	,	,	PUNCT
cana-1285	238	34	s.	s.	PROPN
cana-1285	238	35	e.	e.	PROPN
cana-1285	238	36	(	(	PUNCT
cana-1285	238	37	2022	2022	NUM
cana-1285	238	38	)	)	PUNCT
cana-1285	238	39	.	.	PUNCT
cana-1285	239	1	blockchain	blockchain	PROPN
cana-1285	239	2	hyperledger	hyperledger	PROPN
cana-1285	239	3	with	with	ADP
cana-1285	239	4	non	non	ADJ
cana-1285	239	5	-	-	ADJ
cana-1285	239	6	linear	linear	ADJ
cana-1285	239	7	machine	machine	NOUN
cana-1285	239	8	learning	learning	NOUN
cana-1285	239	9	:	:	PUNCT
cana-1285	239	10	a	a	DET
cana-1285	239	11	novel	novel	NOUN
cana-1285	239	12	and	and	CCONJ
cana-1285	239	13	secure	secure	VERB
cana-1285	239	14	educational	educational	ADJ
cana-1285	239	15	accreditation	accreditation	NOUN
cana-1285	239	16	registration	registration	NOUN
cana-1285	239	17	and	and	CCONJ
cana-1285	239	18	distributed	distribute	VERB
cana-1285	239	19	ledger	ledger	NOUN
cana-1285	239	20	preservation	preservation	NOUN
cana-1285	239	21	architecture	architecture	NOUN
cana-1285	239	22	.	.	PUNCT
cana-1285	240	1	applied	apply	VERB
cana-1285	240	2	sciences	science	NOUN
cana-1285	240	3	,	,	PUNCT
cana-1285	240	4	12(5	12(5	NUM
cana-1285	240	5	)	)	PUNCT
cana-1285	240	6	,	,	PUNCT
cana-1285	240	7	2534	2534	NUM
cana-1285	240	8	[	[	X
cana-1285	240	9	6	6	NUM
cana-1285	240	10	]	]	PUNCT
cana-1285	240	11	gobinathan	gobinathan	NOUN
cana-1285	240	12	,	,	PUNCT
cana-1285	240	13	b.	b.	PROPN
cana-1285	240	14	,	,	PUNCT
cana-1285	240	15	mukunthan	mukunthan	PROPN
cana-1285	240	16	,	,	PUNCT
cana-1285	240	17	m.	m.	NOUN
cana-1285	240	18	a.	a.	PROPN
cana-1285	240	19	,	,	PUNCT
cana-1285	240	20	surendran	surendran	NOUN
cana-1285	240	21	,	,	PUNCT
cana-1285	240	22	s.	s.	PROPN
cana-1285	240	23	,	,	PUNCT
cana-1285	240	24	somasundaram	somasundaram	PROPN
cana-1285	240	25	,	,	PUNCT
cana-1285	240	26	k.	k.	PROPN
cana-1285	240	27	,	,	PUNCT
cana-1285	240	28	moeed	moeed	PROPN
cana-1285	240	29	,	,	PUNCT
cana-1285	240	30	s.	s.	PROPN
cana-1285	240	31	a.	a.	PROPN
cana-1285	240	32	,	,	PUNCT
cana-1285	240	33	niranjan	niranjan	PROPN
cana-1285	240	34	,	,	PUNCT
cana-1285	240	35	p.	p.	PROPN
cana-1285	240	36	,	,	PUNCT
cana-1285	240	37	...	...	PUNCT
cana-1285	240	38	&	&	CCONJ
cana-1285	240	39	sundramurthy	sundramurthy	PROPN
cana-1285	240	40	,	,	PUNCT
cana-1285	240	41	v.	v.	PROPN
cana-1285	240	42	p.	p.	NOUN
cana-1285	240	43	(	(	PUNCT
cana-1285	240	44	2021	2021	NUM
cana-1285	240	45	)	)	PUNCT
cana-1285	240	46	.	.	PUNCT
cana-1285	241	1	a	a	DET
cana-1285	241	2	novel	novel	ADJ
cana-1285	241	3	method	method	NOUN
cana-1285	241	4	to	to	PART
cana-1285	241	5	solve	solve	VERB
cana-1285	241	6	real	real	ADJ
cana-1285	241	7	time	time	NOUN
cana-1285	241	8	security	security	NOUN
cana-1285	241	9	issues	issue	NOUN
cana-1285	241	10	in	in	ADP
cana-1285	241	11	software	software	NOUN
cana-1285	241	12	industry	industry	NOUN
cana-1285	241	13	using	use	VERB
cana-1285	241	14	advanced	advanced	ADJ
cana-1285	241	15	cryptographic	cryptographic	ADJ
cana-1285	241	16	techniques	technique	NOUN
cana-1285	241	17	.	.	PUNCT
cana-1285	242	1	scientific	scientific	ADJ
cana-1285	242	2	programming	programming	NOUN
cana-1285	242	3	,	,	PUNCT
cana-1285	242	4	2021(1	2021(1	NUM
cana-1285	242	5	)	)	PUNCT
cana-1285	242	6	,	,	PUNCT
cana-1285	242	7	3611182	3611182	NUM
cana-1285	243	1	[	[	X
cana-1285	243	2	7	7	X
cana-1285	243	3	]	]	X
cana-1285	243	4	huang	huang	PROPN
cana-1285	243	5	,	,	PUNCT
cana-1285	243	6	l.	l.	PROPN
cana-1285	243	7	,	,	PUNCT
cana-1285	243	8	sun	sun	PROPN
cana-1285	243	9	,	,	PUNCT
cana-1285	243	10	h.	h.	PROPN
cana-1285	243	11	,	,	PUNCT
cana-1285	243	12	sun	sun	PROPN
cana-1285	243	13	,	,	PUNCT
cana-1285	243	14	l.	l.	PROPN
cana-1285	243	15	,	,	PUNCT
cana-1285	243	16	shi	shi	PROPN
cana-1285	243	17	,	,	PUNCT
cana-1285	243	18	k.	k.	PROPN
cana-1285	243	19	,	,	PUNCT
cana-1285	243	20	chen	chen	PROPN
cana-1285	243	21	,	,	PUNCT
cana-1285	243	22	y.	y.	PROPN
cana-1285	243	23	,	,	PUNCT
cana-1285	243	24	ren	ren	PROPN
cana-1285	243	25	,	,	PUNCT
cana-1285	243	26	x.	x.	NOUN
cana-1285	243	27	,	,	PUNCT
cana-1285	243	28	...	...	PUNCT
cana-1285	243	29	&	&	CCONJ
cana-1285	243	30	wang	wang	PROPN
cana-1285	243	31	,	,	PUNCT
cana-1285	243	32	y.	y.	PROPN
cana-1285	243	33	(	(	PUNCT
cana-1285	243	34	2023	2023	NUM
cana-1285	243	35	)	)	PUNCT
cana-1285	243	36	.	.	PUNCT
cana-1285	244	1	rapid	rapid	ADJ
cana-1285	244	2	,	,	PUNCT
cana-1285	244	3	label	label	NOUN
cana-1285	244	4	-	-	PUNCT
cana-1285	244	5	free	free	ADJ
cana-1285	244	6	histopathological	histopathological	ADJ
cana-1285	244	7	diagnosis	diagnosis	NOUN
cana-1285	244	8	of	of	ADP
cana-1285	244	9	liver	liver	NOUN
cana-1285	244	10	cancer	cancer	NOUN
cana-1285	244	11	based	base	VERB
cana-1285	244	12	on	on	ADP
cana-1285	244	13	raman	raman	NOUN
cana-1285	244	14	spectroscopy	spectroscopy	NOUN
cana-1285	244	15	and	and	CCONJ
cana-1285	244	16	deep	deep	ADJ
cana-1285	244	17	learning	learning	NOUN
cana-1285	244	18	.	.	PUNCT
cana-1285	245	1	nature	nature	NOUN
cana-1285	245	2	communications	communication	NOUN
cana-1285	245	3	,	,	PUNCT
cana-1285	245	4	14(1	14(1	NUM
cana-1285	245	5	)	)	PUNCT
cana-1285	245	6	,	,	PUNCT
cana-1285	245	7	48	48	NUM
cana-1285	245	8	.	.	PUNCT
cana-1285	246	1	communications	communication	NOUN
cana-1285	246	2	on	on	ADP
cana-1285	246	3	applied	apply	VERB
cana-1285	246	4	nonlinear	nonlinear	ADJ
cana-1285	246	5	analysis	analysis	NOUN
cana-1285	246	6	issn	issn	NOUN
cana-1285	246	7	:	:	PUNCT
cana-1285	246	8	1074	1074	NUM
cana-1285	246	9	-	-	PUNCT
cana-1285	246	10	133x	133x	NUM
cana-1285	246	11	vol	vol	NOUN
cana-1285	246	12	31	31	NUM
cana-1285	246	13	no	no	NOUN
cana-1285	246	14	.	.	PUNCT
cana-1285	247	1	7s	7	NOUN
cana-1285	247	2	(	(	PUNCT
cana-1285	247	3	2024	2024	NUM
cana-1285	247	4	)	)	PUNCT
cana-1285	247	5	81	81	NUM
cana-1285	248	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-1285	248	2	[	[	X
cana-1285	248	3	8	8	NUM
cana-1285	248	4	]	]	X
cana-1285	248	5	praghash	praghash	NOUN
cana-1285	248	6	,	,	PUNCT
cana-1285	248	7	k.	k.	PROPN
cana-1285	248	8	,	,	PUNCT
cana-1285	248	9	yuvaraj	yuvaraj	PROPN
cana-1285	248	10	,	,	PUNCT
cana-1285	248	11	n.	n.	PROPN
cana-1285	248	12	,	,	PUNCT
cana-1285	248	13	peter	peter	PROPN
cana-1285	248	14	,	,	PUNCT
cana-1285	248	15	g.	g.	PROPN
cana-1285	248	16	,	,	PUNCT
cana-1285	248	17	stonier	stonier	NOUN
cana-1285	248	18	,	,	PUNCT
cana-1285	248	19	a.	a.	NOUN
cana-1285	248	20	a.	a.	PROPN
cana-1285	248	21	,	,	PUNCT
cana-1285	248	22	&	&	CCONJ
cana-1285	248	23	priya	priya	PROPN
cana-1285	248	24	,	,	PUNCT
cana-1285	248	25	r.	r.	PROPN
cana-1285	248	26	d.	d.	PROPN
cana-1285	248	27	(	(	PUNCT
cana-1285	248	28	2022	2022	NUM
cana-1285	248	29	,	,	PUNCT
cana-1285	248	30	december	december	PROPN
cana-1285	248	31	)	)	PUNCT
cana-1285	248	32	.	.	PUNCT
cana-1285	249	1	financial	financial	ADJ
cana-1285	249	2	big	big	ADJ
cana-1285	249	3	data	datum	NOUN
cana-1285	249	4	analysis	analysis	NOUN
cana-1285	249	5	using	use	VERB
cana-1285	249	6	anti	anti	ADJ
cana-1285	249	7	-	-	ADJ
cana-1285	249	8	tampering	tamper	VERB
cana-1285	249	9	blockchain	blockchain	NOUN
cana-1285	249	10	-	-	PUNCT
cana-1285	249	11	based	base	VERB
cana-1285	249	12	deep	deep	ADJ
cana-1285	249	13	learning	learning	NOUN
cana-1285	249	14	.	.	PUNCT
cana-1285	250	1	in	in	ADP
cana-1285	250	2	international	international	ADJ
cana-1285	250	3	conference	conference	NOUN
cana-1285	250	4	on	on	ADP
cana-1285	250	5	hybrid	hybrid	ADJ
cana-1285	250	6	intelligent	intelligent	ADJ
cana-1285	250	7	systems	system	NOUN
cana-1285	250	8	(	(	PUNCT
cana-1285	250	9	pp	pp	ADJ
cana-1285	250	10	.	.	PUNCT
cana-1285	250	11	1031	1031	NUM
cana-1285	250	12	-	-	SYM
cana-1285	250	13	1040	1040	NUM
cana-1285	250	14	)	)	PUNCT
cana-1285	250	15	.	.	PUNCT
cana-1285	251	1	cham	cham	PROPN
cana-1285	251	2	:	:	PUNCT
cana-1285	251	3	springer	springer	NOUN
cana-1285	251	4	nature	nature	PROPN
cana-1285	251	5	switzerland	switzerland	PROPN
cana-1285	252	1	[	[	X
cana-1285	252	2	9	9	NUM
cana-1285	252	3	]	]	X
cana-1285	252	4	ramkumar	ramkumar	NOUN
cana-1285	252	5	,	,	PUNCT
cana-1285	252	6	m.	m.	NOUN
cana-1285	252	7	,	,	PUNCT
cana-1285	252	8	logeshwaran	logeshwaran	NOUN
cana-1285	252	9	,	,	PUNCT
cana-1285	252	10	j.	j.	PROPN
cana-1285	252	11	,	,	PUNCT
cana-1285	252	12	&	&	CCONJ
cana-1285	252	13	husna	husna	ADJ
cana-1285	252	14	,	,	PUNCT
cana-1285	252	15	t.	t.	PROPN
cana-1285	252	16	(	(	PUNCT
cana-1285	252	17	2022	2022	NUM
cana-1285	252	18	)	)	PUNCT
cana-1285	252	19	.	.	PUNCT
cana-1285	253	1	cea	cea	PROPN
cana-1285	253	2	:	:	PUNCT
cana-1285	253	3	certification	certification	NOUN
cana-1285	253	4	based	base	VERB
cana-1285	253	5	encryption	encryption	NOUN
cana-1285	253	6	algorithm	algorithm	NOUN
cana-1285	253	7	for	for	ADP
cana-1285	253	8	enhanced	enhanced	ADJ
cana-1285	253	9	data	datum	NOUN
cana-1285	253	10	protection	protection	NOUN
cana-1285	253	11	in	in	ADP
cana-1285	253	12	social	social	ADJ
cana-1285	253	13	networks	network	NOUN
cana-1285	253	14	.	.	PUNCT
cana-1285	254	1	fundamentals	fundamental	NOUN
cana-1285	254	2	of	of	ADP
cana-1285	254	3	applied	apply	VERB
cana-1285	254	4	mathematics	mathematic	NOUN
cana-1285	254	5	and	and	CCONJ
cana-1285	254	6	soft	soft	ADJ
cana-1285	254	7	computing	computing	NOUN
cana-1285	254	8	,	,	PUNCT
cana-1285	254	9	1	1	NUM
cana-1285	254	10	,	,	PUNCT
cana-1285	254	11	161	161	NUM
cana-1285	254	12	-	-	SYM
cana-1285	254	13	170	170	NUM
cana-1285	254	14	[	[	X
cana-1285	254	15	10	10	NUM
cana-1285	254	16	]	]	X
cana-1285	254	17	kabir	kabir	PROPN
cana-1285	254	18	,	,	PUNCT
cana-1285	254	19	m.	m.	PROPN
cana-1285	254	20	f.	f.	PROPN
cana-1285	254	21	,	,	PUNCT
cana-1285	254	22	chen	chen	PROPN
cana-1285	254	23	,	,	PUNCT
cana-1285	254	24	t.	t.	PROPN
cana-1285	254	25	,	,	PUNCT
cana-1285	254	26	&	&	CCONJ
cana-1285	254	27	ludwig	ludwig	PROPN
cana-1285	254	28	,	,	PUNCT
cana-1285	254	29	s.	s.	PROPN
cana-1285	254	30	a.	a.	PROPN
cana-1285	254	31	(	(	PUNCT
cana-1285	254	32	2023	2023	NUM
cana-1285	254	33	)	)	PUNCT
cana-1285	254	34	.	.	PUNCT
cana-1285	255	1	a	a	DET
cana-1285	255	2	performance	performance	NOUN
cana-1285	255	3	analysis	analysis	NOUN
cana-1285	255	4	of	of	ADP
cana-1285	255	5	dimensionality	dimensionality	NOUN
cana-1285	255	6	reduction	reduction	NOUN
cana-1285	255	7	algorithms	algorithm	NOUN
cana-1285	255	8	in	in	ADP
cana-1285	255	9	machine	machine	NOUN
cana-1285	255	10	learning	learning	NOUN
cana-1285	255	11	models	model	NOUN
cana-1285	255	12	for	for	ADP
cana-1285	255	13	cancer	cancer	NOUN
cana-1285	255	14	prediction	prediction	NOUN
cana-1285	255	15	.	.	PUNCT
cana-1285	256	1	healthcare	healthcare	NOUN
cana-1285	256	2	analytics	analytic	NOUN
cana-1285	256	3	,	,	PUNCT
cana-1285	256	4	3	3	NUM
cana-1285	256	5	,	,	PUNCT
cana-1285	256	6	100125	100125	NUM
cana-1285	256	7	.	.	PUNCT
cana-1285	257	1	[	[	X
cana-1285	257	2	11	11	NUM
cana-1285	257	3	]	]	PUNCT
cana-1285	257	4	choudhry	choudhry	NOUN
cana-1285	257	5	,	,	PUNCT
cana-1285	257	6	m.	m.	NOUN
cana-1285	257	7	d.	d.	PROPN
cana-1285	257	8	,	,	PUNCT
cana-1285	257	9	sivaraj	sivaraj	PROPN
cana-1285	257	10	,	,	PUNCT
cana-1285	257	11	j.	j.	PROPN
cana-1285	257	12	,	,	PUNCT
cana-1285	257	13	munusamy	munusamy	PROPN
cana-1285	257	14	,	,	PUNCT
cana-1285	257	15	s.	s.	PROPN
cana-1285	257	16	,	,	PUNCT
cana-1285	257	17	muthusamy	muthusamy	PROPN
cana-1285	257	18	,	,	PUNCT
cana-1285	257	19	p.	p.	PROPN
cana-1285	257	20	d.	d.	PROPN
cana-1285	257	21	,	,	PUNCT
cana-1285	257	22	&	&	CCONJ
cana-1285	257	23	saravanan	saravanan	PROPN
cana-1285	257	24	,	,	PUNCT
cana-1285	257	25	v.	v.	PROPN
cana-1285	257	26	(	(	PUNCT
cana-1285	257	27	2024	2024	NUM
cana-1285	257	28	)	)	PUNCT
cana-1285	257	29	.	.	PUNCT
cana-1285	258	1	industry	industry	NOUN
cana-1285	258	2	4.0	4.0	NUM
cana-1285	258	3	in	in	ADP
cana-1285	258	4	manufacturing	manufacturing	NOUN
cana-1285	258	5	,	,	PUNCT
cana-1285	258	6	communication	communication	NOUN
cana-1285	258	7	,	,	PUNCT
cana-1285	258	8	transportation	transportation	NOUN
cana-1285	258	9	,	,	PUNCT
cana-1285	258	10	and	and	CCONJ
cana-1285	258	11	health	health	NOUN
cana-1285	258	12	care	care	NOUN
cana-1285	258	13	.	.	PUNCT
cana-1285	259	1	topics	topic	NOUN
cana-1285	259	2	in	in	ADP
cana-1285	259	3	artificial	artificial	ADJ
cana-1285	259	4	intelligence	intelligence	NOUN
cana-1285	259	5	applied	apply	VERB
cana-1285	259	6	to	to	ADP
cana-1285	259	7	industry	industry	NOUN
cana-1285	259	8	4.0	4.0	NUM
cana-1285	259	9	,	,	PUNCT
cana-1285	259	10	149	149	NUM
cana-1285	259	11	-	-	SYM
cana-1285	259	12	165	165	NUM
cana-1285	259	13	.	.	PUNCT
cana-1285	260	1	[	[	X
cana-1285	260	2	12	12	NUM
cana-1285	260	3	]	]	X
cana-1285	260	4	wang	wang	PROPN
cana-1285	260	5	,	,	PUNCT
cana-1285	260	6	r.	r.	PROPN
cana-1285	260	7	,	,	PUNCT
cana-1285	260	8	jiang	jiang	PROPN
cana-1285	260	9	,	,	PUNCT
cana-1285	260	10	y.	y.	PROPN
cana-1285	260	11	,	,	PUNCT
cana-1285	260	12	jin	jin	PROPN
cana-1285	260	13	,	,	PUNCT
cana-1285	260	14	j.	j.	PROPN
cana-1285	260	15	,	,	PUNCT
cana-1285	260	16	yin	yin	PROPN
cana-1285	260	17	,	,	PUNCT
cana-1285	260	18	c.	c.	PROPN
cana-1285	260	19	,	,	PUNCT
cana-1285	260	20	yu	yu	PROPN
cana-1285	260	21	,	,	PUNCT
cana-1285	260	22	h.	h.	PROPN
cana-1285	260	23	,	,	PUNCT
cana-1285	260	24	wang	wang	PROPN
cana-1285	260	25	,	,	PUNCT
cana-1285	260	26	f.	f.	PROPN
cana-1285	260	27	,	,	PUNCT
cana-1285	260	28	...	...	PUNCT
cana-1285	260	29	&	&	CCONJ
cana-1285	260	30	wei	wei	PROPN
cana-1285	260	31	,	,	PUNCT
cana-1285	260	32	l.	l.	PROPN
cana-1285	260	33	(	(	PUNCT
cana-1285	260	34	2023	2023	NUM
cana-1285	260	35	)	)	PUNCT
cana-1285	260	36	.	.	PUNCT
cana-1285	261	1	deepbio	deepbio	NOUN
cana-1285	261	2	:	:	PUNCT
cana-1285	261	3	an	an	DET
cana-1285	261	4	automated	automate	VERB
cana-1285	261	5	and	and	CCONJ
cana-1285	261	6	interpretable	interpretable	ADJ
cana-1285	261	7	deep	deep	ADJ
cana-1285	261	8	-	-	PUNCT
cana-1285	261	9	learning	learn	VERB
cana-1285	261	10	platform	platform	NOUN
cana-1285	261	11	for	for	ADP
cana-1285	261	12	high	high	ADJ
cana-1285	261	13	-	-	PUNCT
cana-1285	261	14	throughput	throughput	NOUN
cana-1285	261	15	biological	biological	ADJ
cana-1285	261	16	sequence	sequence	NOUN
cana-1285	261	17	prediction	prediction	NOUN
cana-1285	261	18	,	,	PUNCT
cana-1285	261	19	functional	functional	ADJ
cana-1285	261	20	annotation	annotation	NOUN
cana-1285	261	21	and	and	CCONJ
cana-1285	261	22	visualization	visualization	NOUN
cana-1285	261	23	analysis	analysis	NOUN
cana-1285	261	24	.	.	PUNCT
cana-1285	262	1	nucleic	nucleic	ADJ
cana-1285	262	2	acids	acid	NOUN
cana-1285	262	3	research	research	NOUN
cana-1285	262	4	,	,	PUNCT
cana-1285	262	5	51(7	51(7	PROPN
cana-1285	262	6	)	)	PUNCT
cana-1285	262	7	,	,	PUNCT
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cana-1285	262	9	-	-	SYM
cana-1285	262	10	3029	3029	NUM
cana-1285	262	11	.	.	PUNCT
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cana-1285	263	3	]	]	SYM
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cana-1285	263	6	a.	a.	PROPN
cana-1285	263	7	,	,	PUNCT
cana-1285	263	8	singh	singh	PROPN
cana-1285	263	9	,	,	PUNCT
cana-1285	263	10	a.	a.	PROPN
cana-1285	263	11	,	,	PUNCT
cana-1285	263	12	&	&	CCONJ
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cana-1285	263	14	,	,	PUNCT
cana-1285	263	15	v.	v.	PROPN
cana-1285	263	16	k.	k.	PROPN
cana-1285	263	17	(	(	PUNCT
cana-1285	263	18	2023	2023	NUM
cana-1285	263	19	)	)	PUNCT
cana-1285	263	20	.	.	PUNCT
cana-1285	264	1	a	a	DET
cana-1285	264	2	systematic	systematic	ADJ
cana-1285	264	3	review	review	NOUN
cana-1285	264	4	on	on	ADP
cana-1285	264	5	biomarker	biomarker	NOUN
cana-1285	264	6	identification	identification	NOUN
cana-1285	264	7	for	for	ADP
cana-1285	264	8	cancer	cancer	NOUN
cana-1285	264	9	diagnosis	diagnosis	NOUN
cana-1285	264	10	and	and	CCONJ
cana-1285	264	11	prognosis	prognosis	NOUN
cana-1285	264	12	in	in	ADP
cana-1285	264	13	multi	multi	NOUN
cana-1285	264	14	-	-	NOUN
cana-1285	264	15	omics	omic	NOUN
cana-1285	264	16	:	:	PUNCT
cana-1285	264	17	from	from	ADP
cana-1285	264	18	computational	computational	ADJ
cana-1285	264	19	needs	need	NOUN
cana-1285	264	20	to	to	PART
cana-1285	264	21	machine	machine	NOUN
cana-1285	264	22	learning	learning	NOUN
cana-1285	264	23	and	and	CCONJ
cana-1285	264	24	deep	deep	ADJ
cana-1285	264	25	learning	learning	NOUN
cana-1285	264	26	.	.	PUNCT
cana-1285	265	1	archives	archive	NOUN
cana-1285	265	2	of	of	ADP
cana-1285	265	3	computational	computational	ADJ
cana-1285	265	4	methods	method	NOUN
cana-1285	265	5	in	in	ADP
cana-1285	265	6	engineering	engineering	NOUN
cana-1285	265	7	,	,	PUNCT
cana-1285	265	8	30(2	30(2	NUM
cana-1285	265	9	)	)	PUNCT
cana-1285	265	10	,	,	PUNCT
cana-1285	265	11	917	917	NUM
cana-1285	265	12	-	-	SYM
cana-1285	265	13	949	949	NUM
cana-1285	265	14	.	.	PUNCT
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cana-1285	266	2	14	14	NUM
cana-1285	266	3	]	]	X
cana-1285	266	4	zeng	zeng	PROPN
cana-1285	266	5	,	,	PUNCT
cana-1285	266	6	q.	q.	PROPN
cana-1285	266	7	,	,	PUNCT
cana-1285	266	8	chen	chen	PROPN
cana-1285	266	9	,	,	PUNCT
cana-1285	266	10	c.	c.	PROPN
cana-1285	266	11	,	,	PUNCT
cana-1285	266	12	chen	chen	PROPN
cana-1285	266	13	,	,	PUNCT
cana-1285	266	14	c.	c.	PROPN
cana-1285	266	15	,	,	PUNCT
cana-1285	266	16	song	song	NOUN
cana-1285	266	17	,	,	PUNCT
cana-1285	266	18	h.	h.	PROPN
cana-1285	266	19	,	,	PUNCT
cana-1285	266	20	li	li	PROPN
cana-1285	266	21	,	,	PUNCT
cana-1285	266	22	m.	m.	NOUN
cana-1285	266	23	,	,	PUNCT
cana-1285	266	24	yan	yan	PROPN
cana-1285	266	25	,	,	PUNCT
cana-1285	266	26	j.	j.	PROPN
cana-1285	266	27	,	,	PUNCT
cana-1285	266	28	&	&	CCONJ
cana-1285	266	29	lv	lv	PROPN
cana-1285	266	30	,	,	PUNCT
cana-1285	266	31	x.	x.	NOUN
cana-1285	266	32	(	(	PUNCT
cana-1285	266	33	2023	2023	NUM
cana-1285	266	34	)	)	PUNCT
cana-1285	266	35	.	.	PUNCT
cana-1285	267	1	serum	serum	PROPN
cana-1285	267	2	raman	raman	NOUN
cana-1285	267	3	spectroscopy	spectroscopy	NOUN
cana-1285	267	4	combined	combine	VERB
cana-1285	267	5	with	with	ADP
cana-1285	267	6	convolutional	convolutional	ADJ
cana-1285	267	7	neural	neural	ADJ
cana-1285	267	8	network	network	NOUN
cana-1285	267	9	for	for	ADP
cana-1285	267	10	rapid	rapid	ADJ
cana-1285	267	11	diagnosis	diagnosis	NOUN
cana-1285	267	12	of	of	ADP
cana-1285	267	13	her2	her2	PROPN
cana-1285	267	14	-	-	PUNCT
cana-1285	267	15	positive	positive	ADJ
cana-1285	267	16	and	and	CCONJ
cana-1285	267	17	triple	triple	ADJ
cana-1285	267	18	-	-	PUNCT
cana-1285	267	19	negative	negative	ADJ
cana-1285	267	20	breast	breast	NOUN
cana-1285	267	21	cancer	cancer	NOUN
cana-1285	267	22	.	.	PUNCT
cana-1285	268	1	spectrochimica	spectrochimica	PROPN
cana-1285	268	2	acta	acta	PROPN
cana-1285	268	3	part	part	PROPN
cana-1285	268	4	a	a	DET
cana-1285	268	5	:	:	PUNCT
cana-1285	268	6	molecular	molecular	ADJ
cana-1285	268	7	and	and	CCONJ
cana-1285	268	8	biomolecular	biomolecular	ADJ
cana-1285	268	9	spectroscopy	spectroscopy	NOUN
cana-1285	268	10	,	,	PUNCT
cana-1285	268	11	286	286	NUM
cana-1285	268	12	,	,	PUNCT
cana-1285	268	13	122000	122000	NUM
cana-1285	268	14	.	.	PUNCT
cana-1285	269	1	[	[	X
cana-1285	269	2	15	15	NUM
cana-1285	269	3	]	]	X
cana-1285	269	4	du	du	X
cana-1285	269	5	,	,	PUNCT
cana-1285	269	6	z.	z.	PROPN
cana-1285	269	7	,	,	PUNCT
cana-1285	269	8	ding	ding	NOUN
cana-1285	269	9	,	,	PUNCT
cana-1285	269	10	x.	x.	PROPN
cana-1285	269	11	,	,	PUNCT
cana-1285	269	12	xu	xu	PROPN
cana-1285	269	13	,	,	PUNCT
cana-1285	269	14	y.	y.	PROPN
cana-1285	269	15	,	,	PUNCT
cana-1285	269	16	&	&	CCONJ
cana-1285	269	17	li	li	PROPN
cana-1285	269	18	,	,	PUNCT
cana-1285	269	19	y.	y.	PROPN
cana-1285	269	20	(	(	PUNCT
cana-1285	269	21	2023	2023	NUM
cana-1285	269	22	)	)	PUNCT
cana-1285	269	23	.	.	PUNCT
cana-1285	270	1	unidl4biopep	unidl4biopep	NOUN
cana-1285	270	2	:	:	PUNCT
cana-1285	270	3	a	a	DET
cana-1285	270	4	universal	universal	ADJ
cana-1285	270	5	deep	deep	ADJ
cana-1285	270	6	learning	learning	NOUN
cana-1285	270	7	architecture	architecture	NOUN
cana-1285	270	8	for	for	ADP
cana-1285	270	9	binary	binary	ADJ
cana-1285	270	10	classification	classification	NOUN
cana-1285	270	11	in	in	ADP
cana-1285	270	12	peptide	peptide	NOUN
cana-1285	270	13	bioactivity	bioactivity	NOUN
cana-1285	270	14	.	.	PUNCT
cana-1285	271	1	briefings	briefing	NOUN
cana-1285	271	2	in	in	ADP
cana-1285	271	3	bioinformatics	bioinformatics	NOUN
cana-1285	271	4	,	,	PUNCT
cana-1285	271	5	24(3	24(3	NUM
cana-1285	271	6	)	)	PUNCT
cana-1285	271	7	,	,	PUNCT
cana-1285	271	8	bbad135	bbad135	PROPN
cana-1285	271	9	.	.	PUNCT