id	sid	tid	token	lemma	pos
ajst-30168	1	1	academic	academic	ADJ
ajst-30168	1	2	journal	journal	NOUN
ajst-30168	1	3	of	of	ADP
ajst-30168	1	4	science	science	NOUN
ajst-30168	1	5	and	and	CCONJ
ajst-30168	1	6	technology	technology	NOUN
ajst-30168	1	7	issn	issn	NOUN
ajst-30168	1	8	:	:	PUNCT
ajst-30168	1	9	2771	2771	NUM
ajst-30168	1	10	-	-	SYM
ajst-30168	1	11	3032	3032	NUM
ajst-30168	1	12	|	|	NOUN
ajst-30168	1	13	vol	vol	NOUN
ajst-30168	1	14	.	.	PUNCT
ajst-30168	2	1	14	14	NUM
ajst-30168	2	2	,	,	PUNCT
ajst-30168	2	3	no	no	INTJ
ajst-30168	2	4	.	.	NOUN
ajst-30168	2	5	3	3	NUM
ajst-30168	2	6	,	,	PUNCT
ajst-30168	2	7	2025	2025	NUM
ajst-30168	2	8	17	17	NUM
ajst-30168	2	9	5mc	5mc	ADJ
ajst-30168	2	10	detection	detection	NOUN
ajst-30168	2	11	based	base	VERB
ajst-30168	2	12	on	on	ADP
ajst-30168	2	13	deep	deep	ADJ
ajst-30168	2	14	learning	learning	NOUN
ajst-30168	2	15	from	from	ADP
ajst-30168	2	16	nanopore	nanopore	ADJ
ajst-30168	2	17	sequencing	sequence	VERB
ajst-30168	2	18	jiongjiong	jiongjiong	NOUN
ajst-30168	2	19	teng1	teng1	NOUN
ajst-30168	2	20	,	,	PUNCT
ajst-30168	2	21	*	*	PUNCT
ajst-30168	2	22	,	,	PUNCT
ajst-30168	2	23	wei	wei	PROPN
ajst-30168	2	24	he2	he2	PROPN
ajst-30168	2	25	,	,	PUNCT
ajst-30168	2	26	a	a	PRON
ajst-30168	2	27	and	and	CCONJ
ajst-30168	2	28	zhihao	zhihao	PROPN
ajst-30168	2	29	zhang1	zhang1	PROPN
ajst-30168	2	30	,	,	PUNCT
ajst-30168	2	31	b	b	PROPN
ajst-30168	2	32	1	1	NUM
ajst-30168	2	33	school	school	NOUN
ajst-30168	2	34	of	of	ADP
ajst-30168	2	35	control	control	NOUN
ajst-30168	2	36	and	and	CCONJ
ajst-30168	2	37	computer	computer	NOUN
ajst-30168	2	38	engineering	engineering	NOUN
ajst-30168	2	39	,	,	PUNCT
ajst-30168	2	40	north	north	PROPN
ajst-30168	2	41	china	china	PROPN
ajst-30168	2	42	electric	electric	PROPN
ajst-30168	2	43	power	power	PROPN
ajst-30168	2	44	university	university	PROPN
ajst-30168	2	45	;	;	PUNCT
ajst-30168	2	46	beijing	beijing	PROPN
ajst-30168	2	47	,	,	PUNCT
ajst-30168	2	48	100096	100096	NUM
ajst-30168	2	49	,	,	PUNCT
ajst-30168	2	50	china	china	PROPN
ajst-30168	2	51	2	2	NUM
ajst-30168	2	52	school	school	NOUN
ajst-30168	2	53	of	of	ADP
ajst-30168	2	54	computer	computer	NOUN
ajst-30168	2	55	science	science	NOUN
ajst-30168	2	56	,	,	PUNCT
ajst-30168	2	57	key	key	ADJ
ajst-30168	2	58	laboratory	laboratory	NOUN
ajst-30168	2	59	of	of	ADP
ajst-30168	2	60	high	high	ADJ
ajst-30168	2	61	confidence	confidence	NOUN
ajst-30168	2	62	software	software	NOUN
ajst-30168	2	63	technologies	technology	NOUN
ajst-30168	2	64	,	,	PUNCT
ajst-30168	2	65	peking	peking	NOUN
ajst-30168	2	66	university	university	NOUN
ajst-30168	2	67	,	,	PUNCT
ajst-30168	2	68	beijing	beijing	PROPN
ajst-30168	2	69	,	,	PUNCT
ajst-30168	2	70	100871	100871	NUM
ajst-30168	2	71	,	,	PUNCT
ajst-30168	2	72	china	china	PROPN
ajst-30168	2	73	*	*	PUNCT
ajst-30168	2	74	corresponding	correspond	VERB
ajst-30168	2	75	author	author	NOUN
ajst-30168	2	76	:	:	PUNCT
ajst-30168	2	77	18736442503@163.com	18736442503@163.com	NUM
ajst-30168	2	78	,	,	PUNCT
ajst-30168	2	79	aheweibright@gmail.com	aheweibright@gmail.com	PROPN
ajst-30168	2	80	,	,	PUNCT
ajst-30168	2	81	bzzhao132@outlook.com	bzzhao132@outlook.com	X
ajst-30168	3	1	abstract	abstract	NOUN
ajst-30168	3	2	:	:	PUNCT
ajst-30168	3	3	dna	dna	PROPN
ajst-30168	3	4	methylation	methylation	NOUN
ajst-30168	3	5	is	be	AUX
ajst-30168	3	6	a	a	DET
ajst-30168	3	7	key	key	ADJ
ajst-30168	3	8	regulator	regulator	NOUN
ajst-30168	3	9	in	in	ADP
ajst-30168	3	10	diverse	diverse	ADJ
ajst-30168	3	11	biological	biological	ADJ
ajst-30168	3	12	processes	process	NOUN
ajst-30168	3	13	,	,	PUNCT
ajst-30168	3	14	particularly	particularly	ADV
ajst-30168	3	15	in	in	ADP
ajst-30168	3	16	mammals	mammal	NOUN
ajst-30168	3	17	where	where	SCONJ
ajst-30168	3	18	5methylcytosine	5methylcytosine	NUM
ajst-30168	3	19	(	(	PUNCT
ajst-30168	3	20	5mc	5mc	ADJ
ajst-30168	3	21	)	)	PUNCT
ajst-30168	3	22	methylation	methylation	NOUN
ajst-30168	3	23	is	be	AUX
ajst-30168	3	24	the	the	DET
ajst-30168	3	25	predominant	predominant	ADJ
ajst-30168	3	26	form	form	NOUN
ajst-30168	3	27	.	.	PUNCT
ajst-30168	4	1	nanopore	nanopore	ADJ
ajst-30168	4	2	sequencing	sequencing	NOUN
ajst-30168	4	3	has	have	AUX
ajst-30168	4	4	attracted	attract	VERB
ajst-30168	4	5	considerable	considerable	ADJ
ajst-30168	4	6	interest	interest	NOUN
ajst-30168	4	7	due	due	ADP
ajst-30168	4	8	to	to	ADP
ajst-30168	4	9	its	its	PRON
ajst-30168	4	10	capacity	capacity	NOUN
ajst-30168	4	11	for	for	ADP
ajst-30168	4	12	direct	direct	ADJ
ajst-30168	4	13	detection	detection	NOUN
ajst-30168	4	14	of	of	ADP
ajst-30168	4	15	5mc	5mc	ADJ
ajst-30168	4	16	modifications	modification	NOUN
ajst-30168	4	17	.	.	PUNCT
ajst-30168	5	1	nonetheless	nonetheless	ADV
ajst-30168	5	2	,	,	PUNCT
ajst-30168	5	3	its	its	PRON
ajst-30168	5	4	accuracy	accuracy	NOUN
ajst-30168	5	5	in	in	ADP
ajst-30168	5	6	detecting	detect	VERB
ajst-30168	5	7	5mc	5mc	ADJ
ajst-30168	5	8	methylation	methylation	NOUN
ajst-30168	5	9	remains	remain	VERB
ajst-30168	5	10	inferior	inferior	ADJ
ajst-30168	5	11	to	to	PART
ajst-30168	5	12	bisulfite	bisulfite	VERB
ajst-30168	5	13	sequencing	sequencing	NOUN
ajst-30168	5	14	.	.	PUNCT
ajst-30168	6	1	in	in	ADP
ajst-30168	6	2	this	this	DET
ajst-30168	6	3	study	study	NOUN
ajst-30168	6	4	,	,	PUNCT
ajst-30168	6	5	we	we	PRON
ajst-30168	6	6	introduce	introduce	VERB
ajst-30168	6	7	a	a	DET
ajst-30168	6	8	novel	novel	ADJ
ajst-30168	6	9	deep	deep	ADJ
ajst-30168	6	10	learning	learning	NOUN
ajst-30168	6	11	algorithm	algorithm	NOUN
ajst-30168	6	12	,	,	PUNCT
ajst-30168	6	13	"	"	PUNCT
ajst-30168	6	14	single	single	ADJ
ajst-30168	6	15	-	-	PUNCT
ajst-30168	6	16	nano	nano	NOUN
ajst-30168	6	17	,	,	PUNCT
ajst-30168	6	18	"	"	PUNCT
ajst-30168	6	19	which	which	PRON
ajst-30168	6	20	aims	aim	VERB
ajst-30168	6	21	to	to	PART
ajst-30168	6	22	improve	improve	VERB
ajst-30168	6	23	the	the	DET
ajst-30168	6	24	precision	precision	NOUN
ajst-30168	6	25	of	of	ADP
ajst-30168	6	26	5mc	5mc	ADJ
ajst-30168	6	27	methylation	methylation	NOUN
ajst-30168	6	28	detection	detection	NOUN
ajst-30168	6	29	.	.	PUNCT
ajst-30168	7	1	our	our	PRON
ajst-30168	7	2	method	method	NOUN
ajst-30168	7	3	segments	segment	VERB
ajst-30168	7	4	the	the	DET
ajst-30168	7	5	detection	detection	NOUN
ajst-30168	7	6	task	task	NOUN
ajst-30168	7	7	into	into	ADP
ajst-30168	7	8	several	several	ADJ
ajst-30168	7	9	subtasks	subtask	NOUN
ajst-30168	7	10	based	base	VERB
ajst-30168	7	11	on	on	ADP
ajst-30168	7	12	motifs	motif	NOUN
ajst-30168	7	13	,	,	PUNCT
ajst-30168	7	14	which	which	PRON
ajst-30168	7	15	enhances	enhance	VERB
ajst-30168	7	16	both	both	CCONJ
ajst-30168	7	17	the	the	DET
ajst-30168	7	18	accuracy	accuracy	NOUN
ajst-30168	7	19	of	of	ADP
ajst-30168	7	20	predictions	prediction	NOUN
ajst-30168	7	21	and	and	CCONJ
ajst-30168	7	22	the	the	DET
ajst-30168	7	23	efficiency	efficiency	NOUN
ajst-30168	7	24	of	of	ADP
ajst-30168	7	25	computational	computational	ADJ
ajst-30168	7	26	processes	process	NOUN
ajst-30168	7	27	.	.	PUNCT
ajst-30168	8	1	evaluations	evaluation	NOUN
ajst-30168	8	2	on	on	ADP
ajst-30168	8	3	publicly	publicly	ADV
ajst-30168	8	4	available	available	ADJ
ajst-30168	8	5	datasets	dataset	NOUN
ajst-30168	8	6	have	have	AUX
ajst-30168	8	7	shown	show	VERB
ajst-30168	8	8	that	that	SCONJ
ajst-30168	8	9	single	single	ADJ
ajst-30168	8	10	-	-	PUNCT
ajst-30168	8	11	nano	nano	NOUN
ajst-30168	8	12	outperforms	outperform	NOUN
ajst-30168	8	13	existing	exist	VERB
ajst-30168	8	14	algorithms	algorithm	NOUN
ajst-30168	8	15	in	in	ADP
ajst-30168	8	16	terms	term	NOUN
ajst-30168	8	17	of	of	ADP
ajst-30168	8	18	effectiveness	effectiveness	NOUN
ajst-30168	8	19	.	.	PUNCT
ajst-30168	9	1	keywords	keyword	NOUN
ajst-30168	9	2	:	:	PUNCT
ajst-30168	9	3	deep	deep	ADJ
ajst-30168	9	4	learning	learning	NOUN
ajst-30168	9	5	,	,	PUNCT
ajst-30168	9	6	nanopore	nanopore	ADV
ajst-30168	9	7	sequencing	sequence	VERB
ajst-30168	9	8	,	,	PUNCT
ajst-30168	9	9	5	5	NUM
ajst-30168	9	10	-	-	PUNCT
ajst-30168	9	11	methylcytosine	methylcytosine	NOUN
ajst-30168	9	12	(	(	PUNCT
ajst-30168	9	13	5mc	5mc	NOUN
ajst-30168	9	14	)	)	PUNCT
ajst-30168	9	15	,	,	PUNCT
ajst-30168	9	16	dna	dna	PROPN
ajst-30168	9	17	methylation	methylation	NOUN
ajst-30168	9	18	,	,	PUNCT
ajst-30168	9	19	motif	motif	NOUN
ajst-30168	9	20	-	-	PUNCT
ajst-30168	9	21	based	base	VERB
ajst-30168	9	22	detection	detection	NOUN
ajst-30168	9	23	.	.	PUNCT
ajst-30168	10	1	1	1	X
ajst-30168	10	2	.	.	X
ajst-30168	10	3	introduction	introduction	NOUN
ajst-30168	10	4	in	in	ADP
ajst-30168	10	5	the	the	DET
ajst-30168	10	6	realm	realm	NOUN
ajst-30168	10	7	of	of	ADP
ajst-30168	10	8	epigenetics	epigenetic	NOUN
ajst-30168	10	9	,	,	PUNCT
ajst-30168	10	10	5	5	NUM
ajst-30168	10	11	-	-	PUNCT
ajst-30168	10	12	methylcytosine	methylcytosine	NOUN
ajst-30168	10	13	(	(	PUNCT
ajst-30168	10	14	5mc	5mc	ADJ
ajst-30168	10	15	)	)	PUNCT
ajst-30168	10	16	methylation	methylation	NOUN
ajst-30168	10	17	stands	stand	VERB
ajst-30168	10	18	out	out	ADP
ajst-30168	10	19	as	as	ADP
ajst-30168	10	20	the	the	DET
ajst-30168	10	21	most	most	ADV
ajst-30168	10	22	significant	significant	ADJ
ajst-30168	10	23	methylation	methylation	NOUN
ajst-30168	10	24	modification	modification	NOUN
ajst-30168	10	25	in	in	ADP
ajst-30168	10	26	mammals	mammal	NOUN
ajst-30168	10	27	.	.	PUNCT
ajst-30168	11	1	dna	dna	PROPN
ajst-30168	11	2	methylation	methylation	NOUN
ajst-30168	11	3	is	be	AUX
ajst-30168	11	4	pivotal	pivotal	ADJ
ajst-30168	11	5	in	in	ADP
ajst-30168	11	6	key	key	ADJ
ajst-30168	11	7	life	life	NOUN
ajst-30168	11	8	processes	process	NOUN
ajst-30168	11	9	within	within	ADP
ajst-30168	11	10	organisms	organism	NOUN
ajst-30168	11	11	,	,	PUNCT
ajst-30168	11	12	with	with	ADP
ajst-30168	11	13	aberrant	aberrant	ADJ
ajst-30168	11	14	patterns	pattern	NOUN
ajst-30168	11	15	linked	link	VERB
ajst-30168	11	16	to	to	ADP
ajst-30168	11	17	a	a	DET
ajst-30168	11	18	spectrum	spectrum	NOUN
ajst-30168	11	19	of	of	ADP
ajst-30168	11	20	human	human	ADJ
ajst-30168	11	21	diseases	disease	NOUN
ajst-30168	11	22	[	[	X
ajst-30168	11	23	1	1	NUM
ajst-30168	11	24	-	-	SYM
ajst-30168	11	25	3	3	NUM
ajst-30168	11	26	]	]	PUNCT
ajst-30168	11	27	.	.	PUNCT
ajst-30168	12	1	bisulfite	bisulfite	NOUN
ajst-30168	12	2	sequencing	sequencing	NOUN
ajst-30168	12	3	,	,	PUNCT
ajst-30168	12	4	considered	consider	VERB
ajst-30168	12	5	the	the	DET
ajst-30168	12	6	gold	gold	ADJ
ajst-30168	12	7	standard	standard	NOUN
ajst-30168	12	8	for	for	ADP
ajst-30168	12	9	5mc	5mc	ADJ
ajst-30168	12	10	detection	detection	NOUN
ajst-30168	12	11	[	[	X
ajst-30168	12	12	4	4	NUM
ajst-30168	12	13	]	]	PUNCT
ajst-30168	12	14	,	,	PUNCT
ajst-30168	12	15	is	be	AUX
ajst-30168	12	16	not	not	PART
ajst-30168	12	17	without	without	ADP
ajst-30168	12	18	flaws	flaw	NOUN
ajst-30168	12	19	;	;	PUNCT
ajst-30168	12	20	its	its	PRON
ajst-30168	12	21	process	process	NOUN
ajst-30168	12	22	involving	involve	VERB
ajst-30168	12	23	chemical	chemical	ADJ
ajst-30168	12	24	dna	dna	PROPN
ajst-30168	12	25	treatment	treatment	NOUN
ajst-30168	12	26	and	and	CCONJ
ajst-30168	12	27	pcr	pcr	ADJ
ajst-30168	12	28	amplification	amplification	NOUN
ajst-30168	12	29	can	can	AUX
ajst-30168	12	30	introduce	introduce	VERB
ajst-30168	12	31	extraneous	extraneous	ADJ
ajst-30168	12	32	errors	error	NOUN
ajst-30168	12	33	.	.	PUNCT
ajst-30168	13	1	in	in	ADP
ajst-30168	13	2	contrast	contrast	NOUN
ajst-30168	13	3	,	,	PUNCT
ajst-30168	13	4	long	long	ADV
ajst-30168	13	5	-	-	PUNCT
ajst-30168	13	6	read	read	VERB
ajst-30168	13	7	sequencing	sequencing	NOUN
ajst-30168	13	8	technologies	technology	NOUN
ajst-30168	13	9	like	like	ADP
ajst-30168	13	10	oxford	oxford	PROPN
ajst-30168	13	11	nanopore	nanopore	ADV
ajst-30168	13	12	enable	enable	VERB
ajst-30168	13	13	direct	direct	ADJ
ajst-30168	13	14	sequencing	sequencing	NOUN
ajst-30168	13	15	of	of	ADP
ajst-30168	13	16	modified	modify	VERB
ajst-30168	13	17	nucleotides	nucleotide	NOUN
ajst-30168	13	18	,	,	PUNCT
ajst-30168	13	19	circumventing	circumvent	VERB
ajst-30168	13	20	the	the	DET
ajst-30168	13	21	issues	issue	NOUN
ajst-30168	13	22	associated	associate	VERB
ajst-30168	13	23	with	with	ADP
ajst-30168	13	24	bisulfite	bisulfite	NOUN
ajst-30168	13	25	treatment	treatment	NOUN
ajst-30168	13	26	.	.	PUNCT
ajst-30168	14	1	tools	tool	NOUN
ajst-30168	14	2	such	such	ADJ
ajst-30168	14	3	as	as	ADP
ajst-30168	14	4	nanopolish	nanopolish	ADJ
ajst-30168	14	5	[	[	X
ajst-30168	14	6	5	5	NUM
ajst-30168	14	7	]	]	PUNCT
ajst-30168	14	8	leverage	leverage	NOUN
ajst-30168	14	9	hidden	hide	VERB
ajst-30168	14	10	markov	markov	NOUN
ajst-30168	14	11	models	model	NOUN
ajst-30168	14	12	to	to	PART
ajst-30168	14	13	identify	identify	VERB
ajst-30168	14	14	cpg	cpg	NOUN
ajst-30168	14	15	methylation	methylation	NOUN
ajst-30168	14	16	,	,	PUNCT
ajst-30168	14	17	while	while	SCONJ
ajst-30168	14	18	others	other	NOUN
ajst-30168	14	19	like	like	ADP
ajst-30168	14	20	megalodon	megalodon	ADJ
ajst-30168	14	21	,	,	PUNCT
ajst-30168	14	22	deepsignal	deepsignal	ADJ
ajst-30168	14	23	[	[	X
ajst-30168	14	24	6	6	NUM
ajst-30168	14	25	]	]	PUNCT
ajst-30168	14	26	,	,	PUNCT
ajst-30168	14	27	deepmod	deepmod	X
ajst-30168	15	1	[	[	X
ajst-30168	15	2	7	7	NUM
ajst-30168	15	3	]	]	PUNCT
ajst-30168	15	4	,	,	PUNCT
ajst-30168	15	5	and	and	CCONJ
ajst-30168	15	6	rockfish	rockfish	ADJ
ajst-30168	15	7	[	[	X
ajst-30168	15	8	8	8	NUM
ajst-30168	15	9	]	]	PUNCT
ajst-30168	15	10	rely	rely	NOUN
ajst-30168	15	11	on	on	ADP
ajst-30168	15	12	neural	neural	ADJ
ajst-30168	15	13	network	network	NOUN
ajst-30168	15	14	algorithms	algorithm	NOUN
ajst-30168	15	15	for	for	ADP
ajst-30168	15	16	detection	detection	NOUN
ajst-30168	15	17	.	.	PUNCT
ajst-30168	16	1	despite	despite	SCONJ
ajst-30168	16	2	the	the	DET
ajst-30168	16	3	availability	availability	NOUN
ajst-30168	16	4	of	of	ADP
ajst-30168	16	5	multiple	multiple	ADJ
ajst-30168	16	6	deep	deep	ADJ
ajst-30168	16	7	learning	learning	NOUN
ajst-30168	16	8	algorithms	algorithm	NOUN
ajst-30168	16	9	for	for	ADP
ajst-30168	16	10	5mc	5mc	ADJ
ajst-30168	16	11	detection	detection	NOUN
ajst-30168	16	12	,	,	PUNCT
ajst-30168	16	13	their	their	PRON
ajst-30168	16	14	reliance	reliance	NOUN
ajst-30168	16	15	on	on	ADP
ajst-30168	16	16	a	a	DET
ajst-30168	16	17	singular	singular	ADJ
ajst-30168	16	18	model	model	NOUN
ajst-30168	16	19	to	to	PART
ajst-30168	16	20	assess	assess	VERB
ajst-30168	16	21	all	all	DET
ajst-30168	16	22	motifs	motif	NOUN
ajst-30168	16	23	can	can	AUX
ajst-30168	16	24	lead	lead	VERB
ajst-30168	16	25	to	to	ADP
ajst-30168	16	26	lower	low	ADJ
ajst-30168	16	27	accuracy	accuracy	NOUN
ajst-30168	16	28	in	in	ADP
ajst-30168	16	29	specific	specific	ADJ
ajst-30168	16	30	contexts	contexts	NOUN
ajst-30168	16	31	,	,	PUNCT
ajst-30168	16	32	hindering	hinder	VERB
ajst-30168	16	33	the	the	DET
ajst-30168	16	34	improvement	improvement	NOUN
ajst-30168	16	35	of	of	ADP
ajst-30168	16	36	overall	overall	ADJ
ajst-30168	16	37	detection	detection	NOUN
ajst-30168	16	38	precision	precision	NOUN
ajst-30168	16	39	.	.	PUNCT
ajst-30168	17	1	this	this	DET
ajst-30168	17	2	study	study	NOUN
ajst-30168	17	3	introduces	introduce	VERB
ajst-30168	17	4	a	a	DET
ajst-30168	17	5	motif	motif	NOUN
ajst-30168	17	6	-	-	PUNCT
ajst-30168	17	7	centric	centric	NOUN
ajst-30168	17	8	strategy	strategy	NOUN
ajst-30168	17	9	for	for	ADP
ajst-30168	17	10	segmenting	segment	VERB
ajst-30168	17	11	the	the	DET
ajst-30168	17	12	5mc	5mc	ADJ
ajst-30168	17	13	detection	detection	NOUN
ajst-30168	17	14	model	model	NOUN
ajst-30168	17	15	into	into	ADP
ajst-30168	17	16	discrete	discrete	ADJ
ajst-30168	17	17	,	,	PUNCT
ajst-30168	17	18	smaller	small	ADJ
ajst-30168	17	19	models	model	NOUN
ajst-30168	17	20	.	.	PUNCT
ajst-30168	18	1	this	this	DET
ajst-30168	18	2	approach	approach	NOUN
ajst-30168	18	3	simplifies	simplify	VERB
ajst-30168	18	4	the	the	DET
ajst-30168	18	5	problem	problem	NOUN
ajst-30168	18	6	by	by	ADP
ajst-30168	18	7	decomposing	decompose	VERB
ajst-30168	18	8	it	it	PRON
ajst-30168	18	9	into	into	ADP
ajst-30168	18	10	more	more	ADV
ajst-30168	18	11	manageable	manageable	ADJ
ajst-30168	18	12	parts	part	NOUN
ajst-30168	18	13	,	,	PUNCT
ajst-30168	18	14	thereby	thereby	ADV
ajst-30168	18	15	enhancing	enhance	VERB
ajst-30168	18	16	detection	detection	NOUN
ajst-30168	18	17	accuracy	accuracy	NOUN
ajst-30168	18	18	and	and	CCONJ
ajst-30168	18	19	diminishing	diminish	VERB
ajst-30168	18	20	computational	computational	ADJ
ajst-30168	18	21	demands	demand	NOUN
ajst-30168	18	22	in	in	ADP
ajst-30168	18	23	the	the	DET
ajst-30168	18	24	prediction	prediction	NOUN
ajst-30168	18	25	phase	phase	NOUN
ajst-30168	18	26	.	.	PUNCT
ajst-30168	19	1	historically	historically	ADV
ajst-30168	19	2	,	,	PUNCT
ajst-30168	19	3	tools	tool	NOUN
ajst-30168	19	4	have	have	AUX
ajst-30168	19	5	been	be	AUX
ajst-30168	19	6	constructed	construct	VERB
ajst-30168	19	7	using	use	VERB
ajst-30168	19	8	genomic	genomic	ADJ
ajst-30168	19	9	data	datum	NOUN
ajst-30168	19	10	,	,	PUNCT
ajst-30168	19	11	such	such	ADJ
ajst-30168	19	12	as	as	ADP
ajst-30168	19	13	that	that	PRON
ajst-30168	19	14	from	from	ADP
ajst-30168	19	15	escherichia	escherichia	NOUN
ajst-30168	19	16	coli	coli	NOUN
ajst-30168	19	17	.	.	PUNCT
ajst-30168	20	1	however	however	ADV
ajst-30168	20	2	,	,	PUNCT
ajst-30168	20	3	repetitive	repetitive	ADJ
ajst-30168	20	4	sequences	sequence	NOUN
ajst-30168	20	5	within	within	ADP
ajst-30168	20	6	genomes	genome	NOUN
ajst-30168	20	7	often	often	ADV
ajst-30168	20	8	lead	lead	VERB
ajst-30168	20	9	to	to	ADP
ajst-30168	20	10	alignment	alignment	NOUN
ajst-30168	20	11	errors	error	NOUN
ajst-30168	20	12	during	during	ADP
ajst-30168	20	13	dataset	dataset	NOUN
ajst-30168	20	14	assembly	assembly	NOUN
ajst-30168	20	15	,	,	PUNCT
ajst-30168	20	16	compromising	compromise	VERB
ajst-30168	20	17	dataset	dataset	ADJ
ajst-30168	20	18	quality	quality	NOUN
ajst-30168	20	19	.	.	PUNCT
ajst-30168	21	1	to	to	PART
ajst-30168	21	2	mitigate	mitigate	VERB
ajst-30168	21	3	these	these	DET
ajst-30168	21	4	issues	issue	NOUN
ajst-30168	21	5	,	,	PUNCT
ajst-30168	21	6	this	this	DET
ajst-30168	21	7	research	research	NOUN
ajst-30168	21	8	employs	employ	VERB
ajst-30168	21	9	synthetic	synthetic	ADJ
ajst-30168	21	10	dna	dna	NOUN
ajst-30168	21	11	datasets	dataset	NOUN
ajst-30168	21	12	,	,	PUNCT
ajst-30168	21	13	thereby	thereby	ADV
ajst-30168	21	14	achieving	achieve	VERB
ajst-30168	21	15	a	a	DET
ajst-30168	21	16	new	new	ADJ
ajst-30168	21	17	level	level	NOUN
ajst-30168	21	18	of	of	ADP
ajst-30168	21	19	precision	precision	NOUN
ajst-30168	21	20	in	in	ADP
ajst-30168	21	21	model	model	NOUN
ajst-30168	21	22	accuracy	accuracy	NOUN
ajst-30168	21	23	.	.	PUNCT
ajst-30168	22	1	2	2	X
ajst-30168	22	2	.	.	X
ajst-30168	22	3	artificial	artificial	ADJ
ajst-30168	22	4	dataset	dataset	NOUN
ajst-30168	22	5	construction	construction	NOUN
ajst-30168	22	6	2.1	2.1	NUM
ajst-30168	22	7	.	.	PUNCT
ajst-30168	23	1	dna	dna	PROPN
ajst-30168	23	2	sequence	sequence	NOUN
ajst-30168	23	3	design	design	NOUN
ajst-30168	23	4	in	in	ADP
ajst-30168	23	5	this	this	DET
ajst-30168	23	6	research	research	NOUN
ajst-30168	23	7	,	,	PUNCT
ajst-30168	23	8	we	we	PRON
ajst-30168	23	9	developed	develop	VERB
ajst-30168	23	10	an	an	DET
ajst-30168	23	11	artificial	artificial	ADJ
ajst-30168	23	12	dataset	dataset	NOUN
ajst-30168	23	13	consisting	consist	VERB
ajst-30168	23	14	of	of	ADP
ajst-30168	23	15	four	four	NUM
ajst-30168	23	16	extended	extended	ADJ
ajst-30168	23	17	dna	dna	NOUN
ajst-30168	23	18	strands	strand	NOUN
ajst-30168	23	19	.	.	PUNCT
ajst-30168	24	1	these	these	DET
ajst-30168	24	2	strands	strand	NOUN
ajst-30168	24	3	were	be	AUX
ajst-30168	24	4	constructed	construct	VERB
ajst-30168	24	5	by	by	ADP
ajst-30168	24	6	piecing	piece	VERB
ajst-30168	24	7	together	together	ADP
ajst-30168	24	8	short	short	ADJ
ajst-30168	24	9	sequences	sequence	NOUN
ajst-30168	24	10	centered	center	VERB
ajst-30168	24	11	around	around	ADP
ajst-30168	24	12	the	the	DET
ajst-30168	24	13	motif	motif	NOUN
ajst-30168	24	14	(	(	PUNCT
ajst-30168	24	15	xxcgxx	xxcgxx	PROPN
ajst-30168	24	16	)	)	PUNCT
ajst-30168	24	17	,	,	PUNCT
ajst-30168	24	18	ensuring	ensure	VERB
ajst-30168	24	19	that	that	SCONJ
ajst-30168	24	20	each	each	DET
ajst-30168	24	21	strand	strand	NOUN
ajst-30168	24	22	contains	contain	VERB
ajst-30168	24	23	a	a	DET
ajst-30168	24	24	specific	specific	ADJ
ajst-30168	24	25	number	number	NOUN
ajst-30168	24	26	of	of	ADP
ajst-30168	24	27	motifs	motif	NOUN
ajst-30168	24	28	covering	cover	VERB
ajst-30168	24	29	all	all	DET
ajst-30168	24	30	those	those	PRON
ajst-30168	24	31	needed	need	VERB
ajst-30168	24	32	for	for	ADP
ajst-30168	24	33	training	training	NOUN
ajst-30168	24	34	.	.	PUNCT
ajst-30168	25	1	figure	figure	NOUN
ajst-30168	25	2	1	1	NUM
ajst-30168	25	3	.	.	PUNCT
ajst-30168	26	1	the	the	DET
ajst-30168	26	2	structure	structure	NOUN
ajst-30168	26	3	of	of	ADP
ajst-30168	26	4	long	long	ADJ
ajst-30168	26	5	dna	dna	PROPN
ajst-30168	26	6	strands	strand	NOUN
ajst-30168	26	7	as	as	SCONJ
ajst-30168	26	8	illustrated	illustrate	VERB
ajst-30168	26	9	in	in	ADP
ajst-30168	26	10	the	the	DET
ajst-30168	26	11	figure1	figure1	PROPN
ajst-30168	26	12	,	,	PUNCT
ajst-30168	26	13	the	the	DET
ajst-30168	26	14	structure	structure	NOUN
ajst-30168	26	15	of	of	ADP
ajst-30168	26	16	these	these	DET
ajst-30168	26	17	strands	strand	NOUN
ajst-30168	26	18	is	be	AUX
ajst-30168	26	19	segmented	segment	VERB
ajst-30168	26	20	into	into	ADP
ajst-30168	26	21	three	three	NUM
ajst-30168	26	22	distinct	distinct	ADJ
ajst-30168	26	23	sections	section	NOUN
ajst-30168	26	24	:	:	PUNCT
ajst-30168	26	25	a	a	DET
ajst-30168	26	26	barcode	barcode	NOUN
ajst-30168	26	27	,	,	PUNCT
ajst-30168	26	28	a	a	DET
ajst-30168	26	29	protection	protection	NOUN
ajst-30168	26	30	zone	zone	NOUN
ajst-30168	26	31	,	,	PUNCT
ajst-30168	26	32	and	and	CCONJ
ajst-30168	26	33	an	an	DET
ajst-30168	26	34	information	information	NOUN
ajst-30168	26	35	zone	zone	NOUN
ajst-30168	26	36	.	.	PUNCT
ajst-30168	27	1	the	the	DET
ajst-30168	27	2	barcode	barcode	NOUN
ajst-30168	27	3	is	be	AUX
ajst-30168	27	4	a	a	DET
ajst-30168	27	5	unique	unique	ADJ
ajst-30168	27	6	dna	dna	NOUN
ajst-30168	27	7	sequence	sequence	NOUN
ajst-30168	27	8	that	that	PRON
ajst-30168	27	9	uniquely	uniquely	ADV
ajst-30168	27	10	identifies	identify	VERB
ajst-30168	27	11	each	each	DET
ajst-30168	27	12	strand	strand	NOUN
ajst-30168	27	13	,	,	PUNCT
ajst-30168	27	14	spanning	span	VERB
ajst-30168	27	15	49	49	NUM
ajst-30168	27	16	nucleotides	nucleotide	NOUN
ajst-30168	27	17	.	.	PUNCT
ajst-30168	28	1	throughout	throughout	ADP
ajst-30168	28	2	experimentation	experimentation	NOUN
ajst-30168	28	3	,	,	PUNCT
ajst-30168	28	4	fully	fully	ADV
ajst-30168	28	5	methylated	methylate	VERB
ajst-30168	28	6	and	and	CCONJ
ajst-30168	28	7	non	non	ADJ
ajst-30168	28	8	-	-	ADJ
ajst-30168	28	9	methylated	methylated	ADJ
ajst-30168	28	10	dna	dna	NOUN
ajst-30168	28	11	strands	strand	NOUN
ajst-30168	28	12	are	be	AUX
ajst-30168	28	13	differentiated	differentiate	VERB
ajst-30168	28	14	using	use	VERB
ajst-30168	28	15	distinct	distinct	ADJ
ajst-30168	28	16	barcodes	barcode	NOUN
ajst-30168	28	17	.	.	PUNCT
ajst-30168	29	1	the	the	DET
ajst-30168	29	2	information	information	NOUN
ajst-30168	29	3	zone	zone	NOUN
ajst-30168	29	4	incorporates	incorporate	VERB
ajst-30168	29	5	every	every	DET
ajst-30168	29	6	possible	possible	ADJ
ajst-30168	29	7	motif	motif	NOUN
ajst-30168	29	8	,	,	PUNCT
ajst-30168	29	9	separated	separate	VERB
ajst-30168	29	10	by	by	ADP
ajst-30168	29	11	4	4	NUM
ajst-30168	29	12	-	-	SYM
ajst-30168	29	13	6	6	NUM
ajst-30168	29	14	nucleotide	nucleotide	NOUN
ajst-30168	29	15	dna	dna	NOUN
ajst-30168	29	16	random	random	ADJ
ajst-30168	29	17	sequences	sequence	NOUN
ajst-30168	29	18	,	,	PUNCT
ajst-30168	29	19	including	include	VERB
ajst-30168	29	20	all	all	DET
ajst-30168	29	21	motifs	motif	NOUN
ajst-30168	29	22	necessary	necessary	ADJ
ajst-30168	29	23	for	for	ADP
ajst-30168	29	24	database	database	NOUN
ajst-30168	29	25	construction	construction	NOUN
ajst-30168	29	26	,	,	PUNCT
ajst-30168	29	27	with	with	ADP
ajst-30168	29	28	each	each	DET
ajst-30168	29	29	motif	motif	NOUN
ajst-30168	29	30	appearing	appear	VERB
ajst-30168	29	31	only	only	ADV
ajst-30168	29	32	once	once	ADV
ajst-30168	29	33	to	to	PART
ajst-30168	29	34	maintain	maintain	VERB
ajst-30168	29	35	equilibrium	equilibrium	NOUN
ajst-30168	29	36	in	in	ADP
ajst-30168	29	37	the	the	DET
ajst-30168	29	38	training	training	NOUN
ajst-30168	29	39	dataset	dataset	NOUN
ajst-30168	29	40	.	.	PUNCT
ajst-30168	30	1	considering	consider	VERB
ajst-30168	30	2	the	the	DET
ajst-30168	30	3	elevated	elevated	ADJ
ajst-30168	30	4	error	error	NOUN
ajst-30168	30	5	rates	rate	NOUN
ajst-30168	30	6	at	at	ADP
ajst-30168	30	7	sequence	sequence	NOUN
ajst-30168	30	8	termini	termini	NOUN
ajst-30168	30	9	in	in	ADP
ajst-30168	30	10	nanopore	nanopore	ADJ
ajst-30168	30	11	sequencing	sequencing	NOUN
ajst-30168	30	12	,	,	PUNCT
ajst-30168	30	13	we	we	PRON
ajst-30168	30	14	incorporated	incorporate	VERB
ajst-30168	30	15	protection	protection	NOUN
ajst-30168	30	16	zones	zone	NOUN
ajst-30168	30	17	at	at	ADP
ajst-30168	30	18	both	both	DET
ajst-30168	30	19	ends	end	NOUN
ajst-30168	30	20	of	of	ADP
ajst-30168	30	21	the	the	DET
ajst-30168	30	22	strands	strand	NOUN
ajst-30168	30	23	to	to	PART
ajst-30168	30	24	safeguard	safeguard	VERB
ajst-30168	30	25	the	the	DET
ajst-30168	30	26	central	central	ADJ
ajst-30168	30	27	information	information	NOUN
ajst-30168	30	28	region	region	NOUN
ajst-30168	30	29	,	,	PUNCT
ajst-30168	30	30	thereby	thereby	ADV
ajst-30168	30	31	ensuring	ensure	VERB
ajst-30168	30	32	that	that	SCONJ
ajst-30168	30	33	the	the	DET
ajst-30168	30	34	nanopore	nanopore	ADJ
ajst-30168	30	35	signals	signal	NOUN
ajst-30168	30	36	from	from	ADP
ajst-30168	30	37	this	this	DET
ajst-30168	30	38	zone	zone	NOUN
ajst-30168	30	39	are	be	AUX
ajst-30168	30	40	adequately	adequately	ADV
ajst-30168	30	41	reliable	reliable	ADJ
ajst-30168	30	42	.	.	PUNCT
ajst-30168	31	1	18	18	NUM
ajst-30168	31	2	2.2	2.2	NUM
ajst-30168	31	3	.	.	PUNCT
ajst-30168	31	4	methylation	methylation	NOUN
ajst-30168	31	5	experiment	experiment	NOUN
ajst-30168	31	6	we	we	PRON
ajst-30168	31	7	utilized	utilize	VERB
ajst-30168	31	8	the	the	DET
ajst-30168	31	9	enzyme	enzyme	NOUN
ajst-30168	31	10	m.sssi	m.sssi	ADJ
ajst-30168	31	11	(	(	PUNCT
ajst-30168	31	12	neb	neb	PROPN
ajst-30168	31	13	,	,	PUNCT
ajst-30168	31	14	m0226s	m0226s	PROPN
ajst-30168	31	15	)	)	PUNCT
ajst-30168	31	16	for	for	ADP
ajst-30168	31	17	the	the	DET
ajst-30168	31	18	preparation	preparation	NOUN
ajst-30168	31	19	of	of	ADP
ajst-30168	31	20	fully	fully	ADV
ajst-30168	31	21	methylated	methylate	VERB
ajst-30168	31	22	dna	dna	NOUN
ajst-30168	31	23	samples	sample	NOUN
ajst-30168	31	24	.	.	PUNCT
ajst-30168	32	1	this	this	DET
ajst-30168	32	2	enzyme	enzyme	NOUN
ajst-30168	32	3	converts	convert	VERB
ajst-30168	32	4	all	all	DET
ajst-30168	32	5	cytosines	cytosine	NOUN
ajst-30168	32	6	at	at	ADP
ajst-30168	32	7	cg	cg	NOUN
ajst-30168	32	8	sites	site	NOUN
ajst-30168	32	9	to	to	ADP
ajst-30168	32	10	5	5	NUM
ajst-30168	32	11	-	-	PUNCT
ajst-30168	32	12	methylcytosine	methylcytosine	NOUN
ajst-30168	32	13	(	(	PUNCT
ajst-30168	32	14	5mc	5mc	NOUN
ajst-30168	32	15	)	)	PUNCT
ajst-30168	32	16	,	,	PUNCT
ajst-30168	32	17	effecting	effect	VERB
ajst-30168	32	18	dna	dna	PROPN
ajst-30168	32	19	methylation	methylation	NOUN
ajst-30168	32	20	.	.	PUNCT
ajst-30168	33	1	the	the	DET
ajst-30168	33	2	long	long	ADJ
ajst-30168	33	3	dna	dna	PROPN
ajst-30168	33	4	strands	strand	NOUN
ajst-30168	33	5	intended	intend	VERB
ajst-30168	33	6	for	for	ADP
ajst-30168	33	7	methylation	methylation	NOUN
ajst-30168	33	8	were	be	AUX
ajst-30168	33	9	synthesized	synthesize	VERB
ajst-30168	33	10	by	by	ADP
ajst-30168	33	11	tsingke	tsingke	NOUN
ajst-30168	33	12	bio	bio	PROPN
ajst-30168	33	13	and	and	CCONJ
ajst-30168	33	14	were	be	AUX
ajst-30168	33	15	embedded	embed	VERB
ajst-30168	33	16	in	in	ADP
ajst-30168	33	17	plasmids	plasmid	NOUN
ajst-30168	33	18	.	.	PUNCT
ajst-30168	34	1	the	the	DET
ajst-30168	34	2	required	require	VERB
ajst-30168	34	3	dna	dna	NOUN
ajst-30168	34	4	strands	strand	NOUN
ajst-30168	34	5	were	be	AUX
ajst-30168	34	6	first	first	ADV
ajst-30168	34	7	amplified	amplify	VERB
ajst-30168	34	8	via	via	ADP
ajst-30168	34	9	pcr	pcr	PROPN
ajst-30168	34	10	.	.	PUNCT
ajst-30168	35	1	while	while	SCONJ
ajst-30168	35	2	the	the	DET
ajst-30168	35	3	positive	positive	ADJ
ajst-30168	35	4	samples	sample	NOUN
ajst-30168	35	5	underwent	undergo	VERB
ajst-30168	35	6	m.sssi	m.sssi	ADJ
ajst-30168	35	7	treatment	treatment	NOUN
ajst-30168	35	8	for	for	ADP
ajst-30168	35	9	complete	complete	ADJ
ajst-30168	35	10	methylation	methylation	NOUN
ajst-30168	35	11	,	,	PUNCT
ajst-30168	35	12	the	the	DET
ajst-30168	35	13	negative	negative	ADJ
ajst-30168	35	14	samples	sample	NOUN
ajst-30168	35	15	remained	remain	VERB
ajst-30168	35	16	untreated	untreated	ADJ
ajst-30168	35	17	.	.	PUNCT
ajst-30168	36	1	these	these	DET
ajst-30168	36	2	samples	sample	NOUN
ajst-30168	36	3	were	be	AUX
ajst-30168	36	4	then	then	ADV
ajst-30168	36	5	combined	combine	VERB
ajst-30168	36	6	and	and	CCONJ
ajst-30168	36	7	analyzed	analyze	VERB
ajst-30168	36	8	using	use	VERB
ajst-30168	36	9	nanopore	nanopore	ADV
ajst-30168	36	10	sequencing	sequencing	NOUN
ajst-30168	36	11	with	with	ADP
ajst-30168	36	12	the	the	DET
ajst-30168	36	13	r10.4.1	r10.4.1	PROPN
ajst-30168	36	14	kit	kit	NOUN
ajst-30168	36	15	,	,	PUNCT
ajst-30168	36	16	with	with	ADP
ajst-30168	36	17	distinct	distinct	ADJ
ajst-30168	36	18	barcodes	barcode	NOUN
ajst-30168	36	19	used	use	VERB
ajst-30168	36	20	to	to	PART
ajst-30168	36	21	differentiate	differentiate	VERB
ajst-30168	36	22	between	between	ADP
ajst-30168	36	23	negative	negative	ADJ
ajst-30168	36	24	and	and	CCONJ
ajst-30168	36	25	positive	positive	ADJ
ajst-30168	36	26	samples	sample	NOUN
ajst-30168	36	27	(	(	PUNCT
ajst-30168	36	28	figure	figure	NOUN
ajst-30168	36	29	2	2	NUM
ajst-30168	36	30	)	)	PUNCT
ajst-30168	36	31	.	.	PUNCT
ajst-30168	37	1	this	this	DET
ajst-30168	37	2	approach	approach	NOUN
ajst-30168	37	3	ensures	ensure	VERB
ajst-30168	37	4	a	a	DET
ajst-30168	37	5	clear	clear	ADJ
ajst-30168	37	6	distinction	distinction	NOUN
ajst-30168	37	7	and	and	CCONJ
ajst-30168	37	8	analysis	analysis	NOUN
ajst-30168	37	9	of	of	ADP
ajst-30168	37	10	methylated	methylate	VERB
ajst-30168	37	11	and	and	CCONJ
ajst-30168	37	12	non	non	ADJ
ajst-30168	37	13	-	-	ADJ
ajst-30168	37	14	methylated	methylate	VERB
ajst-30168	37	15	regions	region	NOUN
ajst-30168	37	16	within	within	ADP
ajst-30168	37	17	the	the	DET
ajst-30168	37	18	dna	dna	PROPN
ajst-30168	37	19	samples	sample	NOUN
ajst-30168	37	20	,	,	PUNCT
ajst-30168	37	21	providing	provide	VERB
ajst-30168	37	22	valuable	valuable	ADJ
ajst-30168	37	23	data	datum	NOUN
ajst-30168	37	24	for	for	ADP
ajst-30168	37	25	epigenetic	epigenetic	ADJ
ajst-30168	37	26	studies	study	NOUN
ajst-30168	37	27	.	.	PUNCT
ajst-30168	38	1	figure	figure	NOUN
ajst-30168	38	2	2	2	NUM
ajst-30168	38	3	.	.	PUNCT
ajst-30168	38	4	sample	sample	NOUN
ajst-30168	38	5	preparation	preparation	NOUN
ajst-30168	38	6	2.3	2.3	NUM
ajst-30168	38	7	.	.	PUNCT
ajst-30168	39	1	feature	feature	NOUN
ajst-30168	39	2	extraction	extraction	NOUN
ajst-30168	39	3	our	our	PRON
ajst-30168	39	4	artificial	artificial	ADJ
ajst-30168	39	5	dataset	dataset	NOUN
ajst-30168	39	6	includes	include	VERB
ajst-30168	39	7	a	a	DET
ajst-30168	39	8	comprehensive	comprehensive	ADJ
ajst-30168	39	9	set	set	NOUN
ajst-30168	39	10	of	of	ADP
ajst-30168	39	11	features	feature	NOUN
ajst-30168	39	12	:	:	PUNCT
ajst-30168	39	13	base	base	NOUN
ajst-30168	39	14	sequences	sequence	NOUN
ajst-30168	39	15	,	,	PUNCT
ajst-30168	39	16	raw	raw	ADJ
ajst-30168	39	17	current	current	ADJ
ajst-30168	39	18	signals	signal	NOUN
ajst-30168	39	19	,	,	PUNCT
ajst-30168	39	20	along	along	ADP
ajst-30168	39	21	with	with	ADP
ajst-30168	39	22	their	their	PRON
ajst-30168	39	23	respective	respective	ADJ
ajst-30168	39	24	lengths	length	NOUN
ajst-30168	39	25	,	,	PUNCT
ajst-30168	39	26	averages	average	NOUN
ajst-30168	39	27	,	,	PUNCT
ajst-30168	39	28	medians	median	NOUN
ajst-30168	39	29	,	,	PUNCT
ajst-30168	39	30	standard	standard	ADJ
ajst-30168	39	31	deviations	deviation	NOUN
ajst-30168	39	32	,	,	PUNCT
ajst-30168	39	33	base	base	NOUN
ajst-30168	39	34	qualities	quality	NOUN
ajst-30168	39	35	,	,	PUNCT
ajst-30168	39	36	and	and	CCONJ
ajst-30168	39	37	basecalling	basecalle	VERB
ajst-30168	39	38	results	result	NOUN
ajst-30168	39	39	characterized	characterize	VERB
ajst-30168	39	40	by	by	ADP
ajst-30168	39	41	insertions	insertion	NOUN
ajst-30168	39	42	,	,	PUNCT
ajst-30168	39	43	deletions	deletion	NOUN
ajst-30168	39	44	,	,	PUNCT
ajst-30168	39	45	and	and	CCONJ
ajst-30168	39	46	substitutions(table	substitutions(table	ADJ
ajst-30168	39	47	1).upon	1).upon	NUM
ajst-30168	39	48	acquiring	acquire	VERB
ajst-30168	39	49	the	the	DET
ajst-30168	39	50	nanopore	nanopore	ADJ
ajst-30168	39	51	detection	detection	NOUN
ajst-30168	39	52	signals	signal	NOUN
ajst-30168	39	53	,	,	PUNCT
ajst-30168	39	54	we	we	PRON
ajst-30168	39	55	utilized	utilize	VERB
ajst-30168	39	56	dorado	dorado	NOUN
ajst-30168	39	57	for	for	ADP
ajst-30168	39	58	conducting	conduct	VERB
ajst-30168	39	59	basecalling	basecalle	VERB
ajst-30168	39	60	and	and	CCONJ
ajst-30168	39	61	mapping	mapping	NOUN
ajst-30168	39	62	of	of	ADP
ajst-30168	39	63	the	the	DET
ajst-30168	39	64	nanopore	nanopore	ADV
ajst-30168	39	65	sequencing	sequence	VERB
ajst-30168	39	66	current	current	ADJ
ajst-30168	39	67	signals	signal	NOUN
ajst-30168	39	68	.	.	PUNCT
ajst-30168	40	1	this	this	DET
ajst-30168	40	2	approach	approach	NOUN
ajst-30168	40	3	facilitated	facilitate	VERB
ajst-30168	40	4	the	the	DET
ajst-30168	40	5	establishment	establishment	NOUN
ajst-30168	40	6	of	of	ADP
ajst-30168	40	7	a	a	DET
ajst-30168	40	8	mapping	mapping	NOUN
ajst-30168	40	9	relationship	relationship	NOUN
ajst-30168	40	10	between	between	ADP
ajst-30168	40	11	the	the	DET
ajst-30168	40	12	current	current	ADJ
ajst-30168	40	13	signals	signal	NOUN
ajst-30168	40	14	and	and	CCONJ
ajst-30168	40	15	the	the	DET
ajst-30168	40	16	corresponding	corresponding	ADJ
ajst-30168	40	17	bases	basis	NOUN
ajst-30168	40	18	.	.	PUNCT
ajst-30168	41	1	this	this	DET
ajst-30168	41	2	refined	refined	ADJ
ajst-30168	41	3	version	version	NOUN
ajst-30168	41	4	enhances	enhance	VERB
ajst-30168	41	5	clarity	clarity	NOUN
ajst-30168	41	6	by	by	ADP
ajst-30168	41	7	specifying	specify	VERB
ajst-30168	41	8	the	the	DET
ajst-30168	41	9	types	type	NOUN
ajst-30168	41	10	of	of	ADP
ajst-30168	41	11	features	feature	NOUN
ajst-30168	41	12	included	include	VERB
ajst-30168	41	13	in	in	ADP
ajst-30168	41	14	the	the	DET
ajst-30168	41	15	dataset	dataset	NOUN
ajst-30168	41	16	and	and	CCONJ
ajst-30168	41	17	clearly	clearly	ADV
ajst-30168	41	18	describing	describe	VERB
ajst-30168	41	19	the	the	DET
ajst-30168	41	20	process	process	NOUN
ajst-30168	41	21	of	of	ADP
ajst-30168	41	22	using	use	VERB
ajst-30168	41	23	dorado	dorado	NOUN
ajst-30168	41	24	for	for	ADP
ajst-30168	41	25	basecalling	basecalling	NOUN
ajst-30168	41	26	and	and	CCONJ
ajst-30168	41	27	mapping	mapping	NOUN
ajst-30168	41	28	.	.	PUNCT
ajst-30168	42	1	the	the	DET
ajst-30168	42	2	academic	academic	ADJ
ajst-30168	42	3	style	style	NOUN
ajst-30168	42	4	is	be	AUX
ajst-30168	42	5	maintained	maintain	VERB
ajst-30168	42	6	throughout	throughout	ADV
ajst-30168	42	7	,	,	PUNCT
ajst-30168	42	8	with	with	ADP
ajst-30168	42	9	a	a	DET
ajst-30168	42	10	focus	focus	NOUN
ajst-30168	42	11	on	on	ADP
ajst-30168	42	12	precision	precision	NOUN
ajst-30168	42	13	and	and	CCONJ
ajst-30168	42	14	readability	readability	NOUN
ajst-30168	42	15	.	.	PUNCT
ajst-30168	43	1	table	table	NOUN
ajst-30168	43	2	1	1	NUM
ajst-30168	43	3	.	.	PUNCT
ajst-30168	44	1	three	three	NUM
ajst-30168	44	2	scheme	scheme	NOUN
ajst-30168	44	3	comparing	compare	VERB
ajst-30168	44	4	name	name	NOUN
ajst-30168	44	5	size	size	NOUN
ajst-30168	44	6	kmer	kmer	PROPN
ajst-30168	44	7	k	k	PROPN
ajst-30168	44	8	signal	signal	PROPN
ajst-30168	44	9	k*100	k*100	PROPN
ajst-30168	44	10	length	length	NOUN
ajst-30168	44	11	k*1	k*1	PROPN
ajst-30168	44	12	mean	mean	VERB
ajst-30168	44	13	k*1	k*1	ADJ
ajst-30168	44	14	median	median	ADJ
ajst-30168	44	15	k*1	k*1	PROPN
ajst-30168	44	16	std	std	NOUN
ajst-30168	44	17	k*1	k*1	ADJ
ajst-30168	44	18	quality	quality	NOUN
ajst-30168	44	19	k*1	k*1	PROPN
ajst-30168	44	20	mismatch	mismatch	NOUN
ajst-30168	44	21	k*1	k*1	ADJ
ajst-30168	44	22	insertion	insertion	NOUN
ajst-30168	44	23	k*1	k*1	ADJ
ajst-30168	44	24	detection	detection	NOUN
ajst-30168	44	25	k*1	k*1	VERB
ajst-30168	44	26	each	each	DET
ajst-30168	44	27	entry	entry	NOUN
ajst-30168	44	28	in	in	ADP
ajst-30168	44	29	our	our	PRON
ajst-30168	44	30	dataset	dataset	NOUN
ajst-30168	44	31	corresponds	correspond	NOUN
ajst-30168	44	32	to	to	ADP
ajst-30168	44	33	a	a	DET
ajst-30168	44	34	cpg	cpg	NOUN
ajst-30168	44	35	site	site	NOUN
ajst-30168	44	36	,	,	PUNCT
ajst-30168	44	37	with	with	ADP
ajst-30168	44	38	a	a	DET
ajst-30168	44	39	span	span	NOUN
ajst-30168	44	40	of	of	ADP
ajst-30168	44	41	10	10	NUM
ajst-30168	44	42	bases	basis	NOUN
ajst-30168	44	43	on	on	ADP
ajst-30168	44	44	either	either	DET
ajst-30168	44	45	side	side	NOUN
ajst-30168	44	46	of	of	ADP
ajst-30168	44	47	the	the	DET
ajst-30168	44	48	central	central	ADJ
ajst-30168	44	49	cytosine	cytosine	NOUN
ajst-30168	44	50	,	,	PUNCT
ajst-30168	44	51	encompassing	encompass	VERB
ajst-30168	44	52	a	a	DET
ajst-30168	44	53	total	total	NOUN
ajst-30168	44	54	of	of	ADP
ajst-30168	44	55	k=21	k=21	PROPN
ajst-30168	44	56	bases	basis	NOUN
ajst-30168	44	57	.	.	PUNCT
ajst-30168	45	1	for	for	ADP
ajst-30168	45	2	each	each	DET
ajst-30168	45	3	base	base	NOUN
ajst-30168	45	4	within	within	ADP
ajst-30168	45	5	these	these	DET
ajst-30168	45	6	21	21	NUM
ajst-30168	45	7	bases	basis	NOUN
ajst-30168	45	8	,	,	PUNCT
ajst-30168	45	9	there	there	PRON
ajst-30168	45	10	is	be	VERB
ajst-30168	45	11	an	an	DET
ajst-30168	45	12	associated	associated	ADJ
ajst-30168	45	13	set	set	NOUN
ajst-30168	45	14	of	of	ADP
ajst-30168	45	15	raw	raw	ADJ
ajst-30168	45	16	current	current	ADJ
ajst-30168	45	17	signals	signal	NOUN
ajst-30168	45	18	denoted	denote	VERB
ajst-30168	45	19	as	as	ADP
ajst-30168	45	20	𝐼	𝐼	PROPN
ajst-30168	45	21	~𝐼	~𝐼	PROPN
ajst-30168	45	22	.	.	PUNCT
ajst-30168	46	1	to	to	PART
ajst-30168	46	2	enrich	enrich	VERB
ajst-30168	46	3	our	our	PRON
ajst-30168	46	4	signal	signal	NOUN
ajst-30168	46	5	features	feature	NOUN
ajst-30168	46	6	,	,	PUNCT
ajst-30168	46	7	we	we	PRON
ajst-30168	46	8	standardize	standardize	VERB
ajst-30168	46	9	the	the	DET
ajst-30168	46	10	length	length	NOUN
ajst-30168	46	11	of	of	ADP
ajst-30168	46	12	the	the	DET
ajst-30168	46	13	current	current	ADJ
ajst-30168	46	14	signal	signal	NOUN
ajst-30168	46	15	𝐿	𝐿	PROPN
ajst-30168	46	16	to	to	ADP
ajst-30168	46	17	100	100	NUM
ajst-30168	46	18	.	.	PUNCT
ajst-30168	47	1	if	if	SCONJ
ajst-30168	47	2	the	the	DET
ajst-30168	47	3	length	length	NOUN
ajst-30168	47	4	of	of	ADP
ajst-30168	47	5	the	the	DET
ajst-30168	47	6	current	current	ADJ
ajst-30168	47	7	signal	signal	NOUN
ajst-30168	47	8	for	for	ADP
ajst-30168	47	9	a	a	DET
ajst-30168	47	10	base	base	NOUN
ajst-30168	47	11	does	do	AUX
ajst-30168	47	12	not	not	PART
ajst-30168	47	13	equal	equal	VERB
ajst-30168	47	14	100	100	NUM
ajst-30168	47	15	,	,	PUNCT
ajst-30168	47	16	we	we	PRON
ajst-30168	47	17	apply	apply	VERB
ajst-30168	47	18	linear	linear	ADJ
ajst-30168	47	19	interpolation	interpolation	NOUN
ajst-30168	47	20	to	to	PART
ajst-30168	47	21	adjust	adjust	VERB
ajst-30168	47	22	the	the	DET
ajst-30168	47	23	signal	signal	NOUN
ajst-30168	47	24	.	.	PUNCT
ajst-30168	48	1	the	the	DET
ajst-30168	48	2	original	original	ADJ
ajst-30168	48	3	time	time	NOUN
ajst-30168	48	4	axis	axis	NOUN
ajst-30168	48	5	for	for	ADP
ajst-30168	48	6	the	the	DET
ajst-30168	48	7	current	current	ADJ
ajst-30168	48	8	signal	signal	NOUN
ajst-30168	48	9	is	be	AUX
ajst-30168	48	10	represented	represent	VERB
ajst-30168	48	11	as	as	ADP
ajst-30168	48	12	𝑡	𝑡	PROPN
ajst-30168	48	13	=	=	NOUN
ajst-30168	48	14	{	{	PUNCT
ajst-30168	48	15	0,1,	0,1,	NOUN
ajst-30168	48	16	…	…	SYM
ajst-30168	48	17	,m-1	,m-1	PUNCT
ajst-30168	48	18	}	}	PUNCT
ajst-30168	48	19	,	,	PUNCT
ajst-30168	48	20	consisting	consist	VERB
ajst-30168	48	21	of	of	ADP
ajst-30168	48	22	m	m	NOUN
ajst-30168	48	23	points	point	NOUN
ajst-30168	48	24	.	.	PUNCT
ajst-30168	49	1	for	for	ADP
ajst-30168	49	2	the	the	DET
ajst-30168	49	3	new	new	ADJ
ajst-30168	49	4	time	time	NOUN
ajst-30168	49	5	axis	axis	PROPN
ajst-30168	49	6	𝑡	𝑡	PROPN
ajst-30168	49	7	,	,	PUNCT
ajst-30168	49	8	each	each	DET
ajst-30168	49	9	point	point	NOUN
ajst-30168	49	10	𝑡	𝑡	PROPN
ajst-30168	49	11	(	(	PUNCT
ajst-30168	49	12	with	with	ADP
ajst-30168	49	13	j∈{0,1	j∈{0,1	NOUN
ajst-30168	49	14	,	,	PUNCT
ajst-30168	49	15	…	…	PUNCT
ajst-30168	49	16	,	,	PUNCT
ajst-30168	49	17	𝐿	𝐿	PROPN
ajst-30168	49	18	}	}	PUNCT
ajst-30168	49	19	)	)	PUNCT
ajst-30168	49	20	is	be	AUX
ajst-30168	49	21	calculated	calculate	VERB
ajst-30168	49	22	using	use	VERB
ajst-30168	49	23	equation	equation	NOUN
ajst-30168	49	24	2	2	NUM
ajst-30168	49	25	-	-	SYM
ajst-30168	49	26	1	1	NUM
ajst-30168	49	27	.	.	PUNCT
ajst-30168	50	1	𝑡	𝑡	PROPN
ajst-30168	50	2	𝑗	𝑗	PROPN
ajst-30168	50	3	(	(	PUNCT
ajst-30168	50	4	2	2	NUM
ajst-30168	50	5	-	-	SYM
ajst-30168	50	6	1	1	NUM
ajst-30168	50	7	)	)	PUNCT
ajst-30168	50	8	for	for	ADP
ajst-30168	50	9	each	each	DET
ajst-30168	50	10	index	index	NOUN
ajst-30168	50	11	j	j	PROPN
ajst-30168	50	12	,	,	PUNCT
ajst-30168	50	13	once	once	SCONJ
ajst-30168	50	14	𝑡	𝑡	PRON
ajst-30168	50	15	is	be	AUX
ajst-30168	50	16	computed	compute	VERB
ajst-30168	50	17	,	,	PUNCT
ajst-30168	50	18	we	we	PRON
ajst-30168	50	19	extract	extract	VERB
ajst-30168	50	20	the	the	DET
ajst-30168	50	21	integer	integer	NOUN
ajst-30168	50	22	part	part	NOUN
ajst-30168	50	23	k	k	X
ajst-30168	50	24	=	=	PUNCT
ajst-30168	50	25	⌊t⌋	⌊t⌋	X
ajst-30168	50	26	and	and	CCONJ
ajst-30168	50	27	the	the	DET
ajst-30168	50	28	decimal	decimal	ADJ
ajst-30168	50	29	part	part	NOUN
ajst-30168	50	30	α	α	X
ajst-30168	50	31	=	=	NOUN
ajst-30168	50	32	t−k	t−k	X
ajst-30168	50	33	.	.	PUNCT
ajst-30168	51	1	if	if	SCONJ
ajst-30168	51	2	k≥m-1	k≥m-1	PROPN
ajst-30168	51	3	,	,	PUNCT
ajst-30168	51	4	we	we	PRON
ajst-30168	51	5	use	use	VERB
ajst-30168	51	6	the	the	DET
ajst-30168	51	7	value	value	NOUN
ajst-30168	51	8	of	of	ADP
ajst-30168	51	9	the	the	DET
ajst-30168	51	10	last	last	ADJ
ajst-30168	51	11	data	datum	NOUN
ajst-30168	51	12	point	point	NOUN
ajst-30168	51	13	𝐼	𝐼	ADV
ajst-30168	51	14	.	.	PUNCT
ajst-30168	52	1	in	in	ADP
ajst-30168	52	2	other	other	ADJ
ajst-30168	52	3	cases	case	NOUN
ajst-30168	52	4	,	,	PUNCT
ajst-30168	52	5	the	the	DET
ajst-30168	52	6	interpolated	interpolate	VERB
ajst-30168	52	7	value	value	NOUN
ajst-30168	52	8	is	be	AUX
ajst-30168	52	9	derived	derive	VERB
ajst-30168	52	10	from	from	ADP
ajst-30168	52	11	a	a	DET
ajst-30168	52	12	linear	linear	ADJ
ajst-30168	52	13	combination	combination	NOUN
ajst-30168	52	14	of	of	ADP
ajst-30168	52	15	the	the	DET
ajst-30168	52	16	two	two	NUM
ajst-30168	52	17	nearest	near	ADJ
ajst-30168	52	18	points(equation	points(equation	NOUN
ajst-30168	52	19	2	2	NUM
ajst-30168	52	20	-	-	SYM
ajst-30168	52	21	2	2	NUM
ajst-30168	52	22	):	):	PUNCT
ajst-30168	52	23	𝐼	𝐼	ADP
ajst-30168	52	24	𝐼	𝐼	PROPN
ajst-30168	52	25	1	1	NUM
ajst-30168	52	26	𝛼	𝛼	NOUN
ajst-30168	52	27	𝐼	𝐼	ADP
ajst-30168	52	28	𝛼	𝛼	PROPN
ajst-30168	52	29	(	(	PUNCT
ajst-30168	52	30	2	2	NUM
ajst-30168	52	31	-	-	SYM
ajst-30168	52	32	2	2	NUM
ajst-30168	52	33	)	)	PUNCT
ajst-30168	52	34	for	for	ADP
ajst-30168	52	35	each	each	DET
ajst-30168	52	36	base	base	NOUN
ajst-30168	52	37	,	,	PUNCT
ajst-30168	52	38	the	the	DET
ajst-30168	52	39	current	current	ADJ
ajst-30168	52	40	signal	signal	NOUN
ajst-30168	52	41	𝐼	𝐼	PROPN
ajst-30168	52	42	(	(	PUNCT
ajst-30168	52	43	j	j	PROPN
ajst-30168	52	44	∈	∈	PROPN
ajst-30168	52	45	{	{	PUNCT
ajst-30168	52	46	0,1	0,1	NOUN
ajst-30168	52	47	,	,	PUNCT
ajst-30168	52	48	…	…	PUNCT
ajst-30168	52	49	,	,	PUNCT
ajst-30168	52	50	𝐿	𝐿	PROPN
ajst-30168	52	51	}	}	PUNCT
ajst-30168	52	52	)	)	PUNCT
ajst-30168	52	53	.the	.the	PUNCT
ajst-30168	53	1	mean	mean	VERB
ajst-30168	53	2	,	,	PUNCT
ajst-30168	53	3	median	median	ADJ
ajst-30168	53	4	,	,	PUNCT
ajst-30168	53	5	and	and	CCONJ
ajst-30168	53	6	standard	standard	ADJ
ajst-30168	53	7	deviation	deviation	NOUN
ajst-30168	53	8	of	of	ADP
ajst-30168	53	9	these	these	DET
ajst-30168	53	10	signals	signal	NOUN
ajst-30168	53	11	are	be	AUX
ajst-30168	53	12	calculated	calculate	VERB
ajst-30168	53	13	based	base	VERB
ajst-30168	53	14	on	on	ADP
ajst-30168	53	15	the	the	DET
ajst-30168	53	16	original	original	ADJ
ajst-30168	53	17	raw	raw	ADJ
ajst-30168	53	18	signals	signal	NOUN
ajst-30168	53	19	𝐼	𝐼	PROPN
ajst-30168	53	20	~𝐼	~𝐼	PROPN
ajst-30168	53	21	.	.	PUNCT
ajst-30168	54	1	base	base	NOUN
ajst-30168	54	2	quality	quality	NOUN
ajst-30168	54	3	assessments	assessment	NOUN
ajst-30168	54	4	and	and	CCONJ
ajst-30168	54	5	basecalling	basecalle	VERB
ajst-30168	54	6	error	error	NOUN
ajst-30168	54	7	metrics	metric	NOUN
ajst-30168	54	8	—	—	PUNCT
ajst-30168	54	9	insertions	insertion	NOUN
ajst-30168	54	10	,	,	PUNCT
ajst-30168	54	11	deletions	deletion	NOUN
ajst-30168	54	12	,	,	PUNCT
ajst-30168	54	13	and	and	CCONJ
ajst-30168	54	14	substitutions	substitution	NOUN
ajst-30168	54	15	—	—	PUNCT
ajst-30168	54	16	are	be	AUX
ajst-30168	54	17	extracted	extract	VERB
ajst-30168	54	18	from	from	ADP
ajst-30168	54	19	alignments	alignment	NOUN
ajst-30168	54	20	in	in	ADP
ajst-30168	54	21	the	the	DET
ajst-30168	54	22	bam	bam	NOUN
ajst-30168	54	23	file	file	NOUN
ajst-30168	54	24	using	use	VERB
ajst-30168	54	25	samtools	samtool	NOUN
ajst-30168	54	26	(	(	PUNCT
ajst-30168	54	27	v1.19.2	v1.19.2	NOUN
ajst-30168	54	28	)	)	PUNCT
ajst-30168	54	29	.	.	PUNCT
ajst-30168	55	1	errors	error	NOUN
ajst-30168	55	2	are	be	AUX
ajst-30168	55	3	coded	code	VERB
ajst-30168	55	4	as	as	ADP
ajst-30168	55	5	binary	binary	ADJ
ajst-30168	55	6	values	value	NOUN
ajst-30168	55	7	:	:	PUNCT
ajst-30168	55	8	0	0	NUM
ajst-30168	55	9	for	for	ADP
ajst-30168	55	10	no	no	DET
ajst-30168	55	11	error	error	NOUN
ajst-30168	55	12	and	and	CCONJ
ajst-30168	55	13	1	1	NUM
ajst-30168	55	14	for	for	ADP
ajst-30168	55	15	the	the	DET
ajst-30168	55	16	presence	presence	NOUN
ajst-30168	55	17	of	of	ADP
ajst-30168	55	18	an	an	DET
ajst-30168	55	19	error	error	NOUN
ajst-30168	55	20	.	.	PUNCT
ajst-30168	56	1	2.4	2.4	NUM
ajst-30168	56	2	.	.	PUNCT
ajst-30168	57	1	dataset	dataset	VERB
ajst-30168	57	2	construction	construction	NOUN
ajst-30168	57	3	following	follow	VERB
ajst-30168	57	4	feature	feature	NOUN
ajst-30168	57	5	extraction	extraction	NOUN
ajst-30168	57	6	,	,	PUNCT
ajst-30168	57	7	the	the	DET
ajst-30168	57	8	dataset	dataset	NOUN
ajst-30168	57	9	is	be	AUX
ajst-30168	57	10	segmented	segment	VERB
ajst-30168	57	11	into	into	ADP
ajst-30168	57	12	sub	sub	NOUN
ajst-30168	57	13	-	-	NOUN
ajst-30168	57	14	datasets	dataset	NOUN
ajst-30168	57	15	according	accord	VERB
ajst-30168	57	16	to	to	ADP
ajst-30168	57	17	various	various	ADJ
ajst-30168	57	18	motifs	motif	NOUN
ajst-30168	57	19	.	.	PUNCT
ajst-30168	58	1	to	to	PART
ajst-30168	58	2	enhance	enhance	VERB
ajst-30168	58	3	the	the	DET
ajst-30168	58	4	precision	precision	NOUN
ajst-30168	58	5	of	of	ADP
ajst-30168	58	6	the	the	DET
ajst-30168	58	7	training	training	NOUN
ajst-30168	58	8	process	process	NOUN
ajst-30168	58	9	,	,	PUNCT
ajst-30168	58	10	superfluous	superfluous	ADJ
ajst-30168	58	11	data	datum	NOUN
ajst-30168	58	12	is	be	AUX
ajst-30168	58	13	eliminated	eliminate	VERB
ajst-30168	58	14	,	,	PUNCT
ajst-30168	58	15	ensuring	ensure	VERB
ajst-30168	58	16	an	an	DET
ajst-30168	58	17	equal	equal	ADJ
ajst-30168	58	18	distribution	distribution	NOUN
ajst-30168	58	19	of	of	ADP
ajst-30168	58	20	negative	negative	ADJ
ajst-30168	58	21	and	and	CCONJ
ajst-30168	58	22	positive	positive	ADJ
ajst-30168	58	23	samples	sample	NOUN
ajst-30168	58	24	,	,	PUNCT
ajst-30168	58	25	each	each	PRON
ajst-30168	58	26	comprising	comprise	VERB
ajst-30168	58	27	50	50	NUM
ajst-30168	58	28	%	%	NOUN
ajst-30168	58	29	within	within	ADP
ajst-30168	58	30	the	the	DET
ajst-30168	58	31	subdatasets	subdataset	NOUN
ajst-30168	58	32	.	.	PUNCT
ajst-30168	59	1	each	each	DET
ajst-30168	59	2	sub	sub	ADJ
ajst-30168	59	3	-	-	ADJ
ajst-30168	59	4	dataset	dataset	ADJ
ajst-30168	59	5	is	be	AUX
ajst-30168	59	6	then	then	ADV
ajst-30168	59	7	further	far	ADV
ajst-30168	59	8	divided	divide	VERB
ajst-30168	59	9	,	,	PUNCT
ajst-30168	59	10	allocating	allocate	VERB
ajst-30168	59	11	90	90	NUM
ajst-30168	59	12	%	%	NOUN
ajst-30168	59	13	for	for	ADP
ajst-30168	59	14	training	training	NOUN
ajst-30168	59	15	purposes	purpose	NOUN
ajst-30168	59	16	and	and	CCONJ
ajst-30168	59	17	the	the	DET
ajst-30168	59	18	remaining	remain	VERB
ajst-30168	59	19	10	10	NUM
ajst-30168	59	20	%	%	NOUN
ajst-30168	59	21	for	for	ADP
ajst-30168	59	22	testing	testing	NOUN
ajst-30168	59	23	.	.	PUNCT
ajst-30168	60	1	19	19	NUM
ajst-30168	60	2	figure	figure	NOUN
ajst-30168	60	3	3	3	NUM
ajst-30168	60	4	.	.	PUNCT
ajst-30168	60	5	model	model	NOUN
ajst-30168	60	6	framework	framework	NOUN
ajst-30168	60	7	3	3	NUM
ajst-30168	60	8	.	.	PUNCT
ajst-30168	60	9	model	model	NOUN
ajst-30168	60	10	framework	framework	NOUN
ajst-30168	60	11	after	after	ADP
ajst-30168	60	12	categorizing	categorize	VERB
ajst-30168	60	13	the	the	DET
ajst-30168	60	14	training	training	NOUN
ajst-30168	60	15	set	set	VERB
ajst-30168	60	16	by	by	ADP
ajst-30168	60	17	motifs	motif	NOUN
ajst-30168	60	18	,	,	PUNCT
ajst-30168	60	19	sequence	sequence	NOUN
ajst-30168	60	20	features	feature	NOUN
ajst-30168	60	21	are	be	AUX
ajst-30168	60	22	rendered	render	VERB
ajst-30168	60	23	ineffective	ineffective	ADJ
ajst-30168	60	24	within	within	ADP
ajst-30168	60	25	this	this	DET
ajst-30168	60	26	context	context	NOUN
ajst-30168	60	27	.	.	PUNCT
ajst-30168	61	1	the	the	DET
ajst-30168	61	2	remaining	remain	VERB
ajst-30168	61	3	nine	nine	NUM
ajst-30168	61	4	features	feature	NOUN
ajst-30168	61	5	are	be	AUX
ajst-30168	61	6	classified	classify	VERB
ajst-30168	61	7	into	into	ADP
ajst-30168	61	8	two	two	NUM
ajst-30168	61	9	groups	group	NOUN
ajst-30168	61	10	based	base	VERB
ajst-30168	61	11	on	on	ADP
ajst-30168	61	12	their	their	PRON
ajst-30168	61	13	informational	informational	ADJ
ajst-30168	61	14	density	density	NOUN
ajst-30168	61	15	:	:	PUNCT
ajst-30168	61	16	sparse	sparse	ADJ
ajst-30168	61	17	features	feature	NOUN
ajst-30168	61	18	,	,	PUNCT
ajst-30168	61	19	such	such	ADJ
ajst-30168	61	20	as	as	ADP
ajst-30168	61	21	current	current	ADJ
ajst-30168	61	22	signals	signal	NOUN
ajst-30168	61	23	;	;	PUNCT
ajst-30168	61	24	and	and	CCONJ
ajst-30168	61	25	dense	dense	ADJ
ajst-30168	61	26	features	feature	NOUN
ajst-30168	61	27	,	,	PUNCT
ajst-30168	61	28	including	include	VERB
ajst-30168	61	29	the	the	DET
ajst-30168	61	30	original	original	ADJ
ajst-30168	61	31	current	current	ADJ
ajst-30168	61	32	signal	signal	NOUN
ajst-30168	61	33	's	's	PART
ajst-30168	61	34	length	length	NOUN
ajst-30168	61	35	,	,	PUNCT
ajst-30168	61	36	average	average	ADJ
ajst-30168	61	37	value	value	NOUN
ajst-30168	61	38	,	,	PUNCT
ajst-30168	61	39	median	median	NOUN
ajst-30168	61	40	,	,	PUNCT
ajst-30168	61	41	standard	standard	ADJ
ajst-30168	61	42	deviation	deviation	NOUN
ajst-30168	61	43	,	,	PUNCT
ajst-30168	61	44	base	base	NOUN
ajst-30168	61	45	quality	quality	NOUN
ajst-30168	61	46	,	,	PUNCT
ajst-30168	61	47	and	and	CCONJ
ajst-30168	61	48	error	error	NOUN
ajst-30168	61	49	metrics	metric	NOUN
ajst-30168	61	50	from	from	ADP
ajst-30168	61	51	basecalling	basecalle	VERB
ajst-30168	61	52	(	(	PUNCT
ajst-30168	61	53	insertions	insertion	NOUN
ajst-30168	61	54	,	,	PUNCT
ajst-30168	61	55	deletions	deletion	NOUN
ajst-30168	61	56	,	,	PUNCT
ajst-30168	61	57	and	and	CCONJ
ajst-30168	61	58	substitutions	substitution	NOUN
ajst-30168	61	59	)	)	PUNCT
ajst-30168	61	60	.	.	PUNCT
ajst-30168	62	1	all	all	DET
ajst-30168	62	2	input	input	NOUN
ajst-30168	62	3	features	feature	VERB
ajst-30168	62	4	being	be	AUX
ajst-30168	62	5	sequential	sequential	ADJ
ajst-30168	62	6	in	in	ADP
ajst-30168	62	7	nature	nature	NOUN
ajst-30168	62	8	prompts	prompt	VERB
ajst-30168	62	9	the	the	DET
ajst-30168	62	10	use	use	NOUN
ajst-30168	62	11	of	of	ADP
ajst-30168	62	12	a	a	DET
ajst-30168	62	13	bidirectional	bidirectional	ADJ
ajst-30168	62	14	long	long	ADJ
ajst-30168	62	15	short	short	ADJ
ajst-30168	62	16	-	-	PUNCT
ajst-30168	62	17	term	term	NOUN
ajst-30168	62	18	memory	memory	NOUN
ajst-30168	62	19	(	(	PUNCT
ajst-30168	62	20	bilstm	bilstm	NOUN
ajst-30168	62	21	)	)	PUNCT
ajst-30168	62	22	network	network	NOUN
ajst-30168	62	23	for	for	ADP
ajst-30168	62	24	feature	feature	NOUN
ajst-30168	62	25	extraction	extraction	NOUN
ajst-30168	62	26	.	.	PUNCT
ajst-30168	63	1	given	give	VERB
ajst-30168	63	2	the	the	DET
ajst-30168	63	3	voluminous	voluminous	ADJ
ajst-30168	63	4	nature	nature	NOUN
ajst-30168	63	5	of	of	ADP
ajst-30168	63	6	current	current	ADJ
ajst-30168	63	7	signal	signal	NOUN
ajst-30168	63	8	data	datum	NOUN
ajst-30168	63	9	,	,	PUNCT
ajst-30168	63	10	a	a	DET
ajst-30168	63	11	three	three	NUM
ajst-30168	63	12	-	-	PUNCT
ajst-30168	63	13	layer	layer	NOUN
ajst-30168	63	14	1d	1d	NUM
ajst-30168	63	15	convolutional	convolutional	ADJ
ajst-30168	63	16	neural	neural	ADJ
ajst-30168	63	17	network	network	NOUN
ajst-30168	63	18	(	(	PUNCT
ajst-30168	63	19	1dcnn	1dcnn	NUM
ajst-30168	63	20	)	)	PUNCT
ajst-30168	63	21	is	be	AUX
ajst-30168	63	22	first	first	ADV
ajst-30168	63	23	applied	apply	VERB
ajst-30168	63	24	for	for	ADP
ajst-30168	63	25	local	local	ADJ
ajst-30168	63	26	feature	feature	NOUN
ajst-30168	63	27	extraction	extraction	NOUN
ajst-30168	63	28	to	to	PART
ajst-30168	63	29	alleviate	alleviate	VERB
ajst-30168	63	30	computational	computational	ADJ
ajst-30168	63	31	demands	demand	NOUN
ajst-30168	63	32	,	,	PUNCT
ajst-30168	63	33	succeeded	succeed	VERB
ajst-30168	63	34	by	by	ADP
ajst-30168	63	35	bilstm	bilstm	NOUN
ajst-30168	63	36	for	for	ADP
ajst-30168	63	37	extracting	extract	VERB
ajst-30168	63	38	sequential	sequential	ADJ
ajst-30168	63	39	features	feature	NOUN
ajst-30168	63	40	.	.	PUNCT
ajst-30168	64	1	to	to	PART
ajst-30168	64	2	account	account	VERB
ajst-30168	64	3	for	for	ADP
ajst-30168	64	4	methylation	methylation	NOUN
ajst-30168	64	5	's	's	PART
ajst-30168	64	6	position	position	NOUN
ajst-30168	64	7	-	-	PUNCT
ajst-30168	64	8	dependent	dependent	ADJ
ajst-30168	64	9	impact	impact	NOUN
ajst-30168	64	10	on	on	ADP
ajst-30168	64	11	current	current	ADJ
ajst-30168	64	12	signals	signal	NOUN
ajst-30168	64	13	,	,	PUNCT
ajst-30168	64	14	an	an	DET
ajst-30168	64	15	attention	attention	NOUN
ajst-30168	64	16	mechanism	mechanism	NOUN
ajst-30168	64	17	is	be	AUX
ajst-30168	64	18	employed	employ	VERB
ajst-30168	64	19	to	to	PART
ajst-30168	64	20	assign	assign	VERB
ajst-30168	64	21	weights	weight	NOUN
ajst-30168	64	22	to	to	ADP
ajst-30168	64	23	these	these	DET
ajst-30168	64	24	features	feature	NOUN
ajst-30168	64	25	.	.	PUNCT
ajst-30168	65	1	sparse	sparse	ADJ
ajst-30168	65	2	features	feature	NOUN
ajst-30168	65	3	proceed	proceed	VERB
ajst-30168	65	4	directly	directly	ADV
ajst-30168	65	5	into	into	ADP
ajst-30168	65	6	the	the	DET
ajst-30168	65	7	bilstm.the	bilstm.the	DET
ajst-30168	65	8	two	two	NUM
ajst-30168	65	9	types	type	NOUN
ajst-30168	65	10	of	of	ADP
ajst-30168	65	11	features	feature	NOUN
ajst-30168	65	12	are	be	AUX
ajst-30168	65	13	then	then	ADV
ajst-30168	65	14	integrated	integrate	VERB
ajst-30168	65	15	,	,	PUNCT
ajst-30168	65	16	followed	follow	VERB
ajst-30168	65	17	by	by	ADP
ajst-30168	65	18	five	five	NUM
ajst-30168	65	19	fully	fully	ADV
ajst-30168	65	20	connected	connect	VERB
ajst-30168	65	21	layers	layer	NOUN
ajst-30168	65	22	for	for	ADP
ajst-30168	65	23	classification	classification	NOUN
ajst-30168	65	24	.	.	PUNCT
ajst-30168	66	1	the	the	DET
ajst-30168	66	2	final	final	ADJ
ajst-30168	66	3	step	step	NOUN
ajst-30168	66	4	involves	involve	VERB
ajst-30168	66	5	employing	employ	VERB
ajst-30168	66	6	a	a	DET
ajst-30168	66	7	softmax	softmax	NOUN
ajst-30168	66	8	function	function	NOUN
ajst-30168	66	9	to	to	PART
ajst-30168	66	10	transform	transform	VERB
ajst-30168	66	11	predictions	prediction	NOUN
ajst-30168	66	12	into	into	ADP
ajst-30168	66	13	probabilities	probability	NOUN
ajst-30168	66	14	indicative	indicative	ADJ
ajst-30168	66	15	of	of	ADP
ajst-30168	66	16	methylation	methylation	NOUN
ajst-30168	66	17	.	.	PUNCT
ajst-30168	67	1	4	4	X
ajst-30168	67	2	.	.	X
ajst-30168	67	3	single	single	ADJ
ajst-30168	67	4	motif	motif	NOUN
ajst-30168	67	5	model	model	NOUN
ajst-30168	67	6	previous	previous	ADJ
ajst-30168	67	7	research	research	NOUN
ajst-30168	67	8	typically	typically	ADV
ajst-30168	67	9	employed	employ	VERB
ajst-30168	67	10	a	a	DET
ajst-30168	67	11	single	single	ADJ
ajst-30168	67	12	model	model	NOUN
ajst-30168	67	13	to	to	PART
ajst-30168	67	14	predict	predict	VERB
ajst-30168	67	15	methylation	methylation	NOUN
ajst-30168	67	16	across	across	ADP
ajst-30168	67	17	all	all	DET
ajst-30168	67	18	motifs	motif	NOUN
ajst-30168	67	19	.	.	PUNCT
ajst-30168	68	1	however	however	ADV
ajst-30168	68	2	,	,	PUNCT
ajst-30168	68	3	since	since	SCONJ
ajst-30168	68	4	methylation	methylation	NOUN
ajst-30168	68	5	affects	affect	VERB
ajst-30168	68	6	the	the	DET
ajst-30168	68	7	current	current	ADJ
ajst-30168	68	8	signals	signal	NOUN
ajst-30168	68	9	differently	differently	ADV
ajst-30168	68	10	for	for	ADP
ajst-30168	68	11	each	each	DET
ajst-30168	68	12	motif	motif	NOUN
ajst-30168	68	13	,	,	PUNCT
ajst-30168	68	14	this	this	DET
ajst-30168	68	15	approach	approach	NOUN
ajst-30168	68	16	often	often	ADV
ajst-30168	68	17	resulted	result	VERB
ajst-30168	68	18	in	in	ADP
ajst-30168	68	19	models	model	NOUN
ajst-30168	68	20	favoring	favor	VERB
ajst-30168	68	21	certain	certain	ADJ
ajst-30168	68	22	motifs	motif	NOUN
ajst-30168	68	23	in	in	ADP
ajst-30168	68	24	terms	term	NOUN
ajst-30168	68	25	of	of	ADP
ajst-30168	68	26	accuracy	accuracy	NOUN
ajst-30168	68	27	[	[	X
ajst-30168	68	28	9	9	NUM
ajst-30168	68	29	]	]	PUNCT
ajst-30168	68	30	.	.	PUNCT
ajst-30168	69	1	a	a	DET
ajst-30168	69	2	case	case	NOUN
ajst-30168	69	3	in	in	ADP
ajst-30168	69	4	point	point	NOUN
ajst-30168	69	5	is	be	AUX
ajst-30168	69	6	megalodon	megalodon	PROPN
ajst-30168	69	7	's	's	PART
ajst-30168	69	8	performance	performance	NOUN
ajst-30168	69	9	on	on	ADP
ajst-30168	69	10	the	the	DET
ajst-30168	69	11	human	human	ADJ
ajst-30168	69	12	genome	genome	NOUN
ajst-30168	69	13	(	(	PUNCT
ajst-30168	69	14	na12878	na12878	PROPN
ajst-30168	69	15	)	)	PUNCT
ajst-30168	70	1	[	[	X
ajst-30168	70	2	10	10	NUM
ajst-30168	70	3	]	]	PUNCT
ajst-30168	70	4	,	,	PUNCT
ajst-30168	70	5	where	where	SCONJ
ajst-30168	70	6	it	it	PRON
ajst-30168	70	7	was	be	AUX
ajst-30168	70	8	benchmarked	benchmarke	VERB
ajst-30168	70	9	against	against	ADP
ajst-30168	70	10	bisulfite	bisulfite	NOUN
ajst-30168	70	11	sequencing	sequence	VERB
ajst-30168	70	12	data	datum	NOUN
ajst-30168	70	13	(	(	PUNCT
ajst-30168	70	14	encode	encode	ADJ
ajst-30168	70	15	,	,	PUNCT
ajst-30168	70	16	encff798rss	encff798rss	ADJ
ajst-30168	70	17	and	and	CCONJ
ajst-30168	70	18	encff113krq	encff113krq	ADJ
ajst-30168	70	19	)	)	PUNCT
ajst-30168	70	20	.	.	PUNCT
ajst-30168	71	1	notably	notably	ADV
ajst-30168	71	2	,	,	PUNCT
ajst-30168	71	3	megalodon	megalodon	PROPN
ajst-30168	71	4	's	's	PART
ajst-30168	71	5	prediction	prediction	NOUN
ajst-30168	71	6	accuracy	accuracy	NOUN
ajst-30168	71	7	was	be	AUX
ajst-30168	71	8	significantly	significantly	ADV
ajst-30168	71	9	lower	low	ADJ
ajst-30168	71	10	for	for	ADP
ajst-30168	71	11	the	the	DET
ajst-30168	71	12	ttcgtc	ttcgtc	NOUN
ajst-30168	71	13	motif	motif	NOUN
ajst-30168	71	14	compared	compare	VERB
ajst-30168	71	15	to	to	ADP
ajst-30168	71	16	other	other	ADJ
ajst-30168	71	17	motifs	motif	NOUN
ajst-30168	71	18	.	.	PUNCT
ajst-30168	72	1	figure	figure	NOUN
ajst-30168	72	2	4	4	NUM
ajst-30168	72	3	.	.	PUNCT
ajst-30168	73	1	the	the	DET
ajst-30168	73	2	accuracy	accuracy	NOUN
ajst-30168	73	3	of	of	ADP
ajst-30168	73	4	megalodon	megalodon	PROPN
ajst-30168	73	5	on	on	ADP
ajst-30168	73	6	different	different	ADJ
ajst-30168	73	7	motifs	motif	NOUN
ajst-30168	73	8	in	in	ADP
ajst-30168	73	9	the	the	DET
ajst-30168	73	10	single	single	ADJ
ajst-30168	73	11	-	-	PUNCT
ajst-30168	73	12	nano	nano	NOUN
ajst-30168	73	13	framework	framework	NOUN
ajst-30168	73	14	,	,	PUNCT
ajst-30168	73	15	a	a	DET
ajst-30168	73	16	distinct	distinct	ADJ
ajst-30168	73	17	mini	mini	NOUN
ajst-30168	73	18	-	-	NOUN
ajst-30168	73	19	model	model	NOUN
ajst-30168	73	20	is	be	AUX
ajst-30168	73	21	designated	designate	VERB
ajst-30168	73	22	for	for	ADP
ajst-30168	73	23	each	each	DET
ajst-30168	73	24	motif	motif	NOUN
ajst-30168	73	25	.	.	PUNCT
ajst-30168	74	1	to	to	PART
ajst-30168	74	2	minimize	minimize	VERB
ajst-30168	74	3	training	training	NOUN
ajst-30168	74	4	duration	duration	NOUN
ajst-30168	74	5	,	,	PUNCT
ajst-30168	74	6	a	a	DET
ajst-30168	74	7	20	20	NUM
ajst-30168	74	8	base	base	NOUN
ajst-30168	74	9	model	model	NOUN
ajst-30168	74	10	is	be	AUX
ajst-30168	74	11	first	first	ADV
ajst-30168	74	12	trained	train	VERB
ajst-30168	74	13	on	on	ADP
ajst-30168	74	14	an	an	DET
ajst-30168	74	15	extensive	extensive	ADJ
ajst-30168	74	16	dataset	dataset	NOUN
ajst-30168	74	17	.	.	PUNCT
ajst-30168	75	1	thereafter	thereafter	ADV
ajst-30168	75	2	,	,	PUNCT
ajst-30168	75	3	each	each	DET
ajst-30168	75	4	mini	mini	ADJ
ajst-30168	75	5	-	-	NOUN
ajst-30168	75	6	model	model	ADJ
ajst-30168	75	7	leverages	leverage	NOUN
ajst-30168	75	8	transfer	transfer	NOUN
ajst-30168	75	9	learning	learn	VERB
ajst-30168	75	10	with	with	ADP
ajst-30168	75	11	its	its	PRON
ajst-30168	75	12	specific	specific	ADJ
ajst-30168	75	13	training	training	NOUN
ajst-30168	75	14	set	set	VERB
ajst-30168	75	15	to	to	PART
ajst-30168	75	16	achieve	achieve	VERB
ajst-30168	75	17	more	more	ADV
ajst-30168	75	18	accurate	accurate	ADJ
ajst-30168	75	19	predictions	prediction	NOUN
ajst-30168	75	20	.	.	PUNCT
ajst-30168	76	1	figure	figure	NOUN
ajst-30168	76	2	5	5	NUM
ajst-30168	76	3	.	.	PUNCT
ajst-30168	76	4	model	model	NOUN
ajst-30168	76	5	performance	performance	NOUN
ajst-30168	76	6	evaluation	evaluation	NOUN
ajst-30168	76	7	5	5	NUM
ajst-30168	76	8	.	.	PUNCT
ajst-30168	76	9	model	model	NOUN
ajst-30168	76	10	validation	validation	PROPN
ajst-30168	76	11	5.1	5.1	NUM
ajst-30168	76	12	.	.	PUNCT
ajst-30168	77	1	model	model	NOUN
ajst-30168	77	2	training	training	NOUN
ajst-30168	77	3	this	this	DET
ajst-30168	77	4	section	section	NOUN
ajst-30168	77	5	initiates	initiate	VERB
ajst-30168	77	6	with	with	ADP
ajst-30168	77	7	the	the	DET
ajst-30168	77	8	training	training	NOUN
ajst-30168	77	9	of	of	ADP
ajst-30168	77	10	a	a	DET
ajst-30168	77	11	foundational	foundational	ADJ
ajst-30168	77	12	model	model	NOUN
ajst-30168	77	13	utilizing	utilize	VERB
ajst-30168	77	14	the	the	DET
ajst-30168	77	15	most	most	ADV
ajst-30168	77	16	frequent	frequent	ADJ
ajst-30168	77	17	kmer	kmer	NOUN
ajst-30168	77	18	dataset	dataset	VERB
ajst-30168	77	19	post	post	ADJ
ajst-30168	77	20	-	-	ADJ
ajst-30168	77	21	data	data	ADJ
ajst-30168	77	22	balancing	balancing	NOUN
ajst-30168	77	23	.	.	PUNCT
ajst-30168	78	1	the	the	DET
ajst-30168	78	2	model	model	NOUN
ajst-30168	78	3	parameters	parameter	NOUN
ajst-30168	78	4	are	be	AUX
ajst-30168	78	5	fine	fine	ADV
ajst-30168	78	6	-	-	PUNCT
ajst-30168	78	7	tuned	tune	VERB
ajst-30168	78	8	by	by	ADP
ajst-30168	78	9	minimizing	minimize	VERB
ajst-30168	78	10	cross	cross	NOUN
ajst-30168	78	11	-	-	NOUN
ajst-30168	78	12	entropy	entropy	NOUN
ajst-30168	78	13	on	on	ADP
ajst-30168	78	14	the	the	DET
ajst-30168	78	15	training	training	NOUN
ajst-30168	78	16	dataset	dataset	NOUN
ajst-30168	78	17	.	.	PUNCT
ajst-30168	79	1	the	the	DET
ajst-30168	79	2	formula	formula	NOUN
ajst-30168	79	3	for	for	ADP
ajst-30168	79	4	the	the	DET
ajst-30168	79	5	cross	cross	ADJ
ajst-30168	79	6	-	-	ADJ
ajst-30168	79	7	entropy	entropy	ADJ
ajst-30168	79	8	loss	loss	NOUN
ajst-30168	79	9	function	function	NOUN
ajst-30168	79	10	is	be	AUX
ajst-30168	79	11	presented	present	VERB
ajst-30168	79	12	below(equation	below(equation	PROPN
ajst-30168	79	13	5	5	NUM
ajst-30168	79	14	-	-	SYM
ajst-30168	79	15	1	1	NUM
ajst-30168	79	16	):	):	PUNCT
ajst-30168	79	17	𝐿	𝐿	PROPN
ajst-30168	79	18	𝜃	𝜃	PROPN
ajst-30168	79	19	∑	∑	PROPN
ajst-30168	79	20	𝑦	𝑦	PRON
ajst-30168	79	21	log	log	VERB
ajst-30168	79	22	𝑦	𝑦	PRON
ajst-30168	79	23	1	1	NUM
ajst-30168	79	24	𝑦	𝑦	NOUN
ajst-30168	79	25	log	log	VERB
ajst-30168	79	26	1	1	NUM
ajst-30168	79	27	𝑦	𝑦	NOUN
ajst-30168	79	28	(	(	PUNCT
ajst-30168	79	29	5	5	NUM
ajst-30168	79	30	-	-	SYM
ajst-30168	79	31	1	1	NUM
ajst-30168	79	32	)	)	PUNCT
ajst-30168	79	33	in	in	ADP
ajst-30168	79	34	this	this	DET
ajst-30168	79	35	chapter	chapter	NOUN
ajst-30168	79	36	,	,	PUNCT
ajst-30168	79	37	we	we	PRON
ajst-30168	79	38	utilize	utilize	VERB
ajst-30168	79	39	the	the	DET
ajst-30168	79	40	adam	adam	PROPN
ajst-30168	79	41	optimizer	optimizer	NOUN
ajst-30168	79	42	with	with	ADP
ajst-30168	79	43	a	a	DET
ajst-30168	79	44	learning	learn	VERB
ajst-30168	79	45	rate	rate	NOUN
ajst-30168	79	46	of	of	ADP
ajst-30168	79	47	0.001	0.001	NUM
ajst-30168	79	48	to	to	PART
ajst-30168	79	49	train	train	VERB
ajst-30168	79	50	the	the	DET
ajst-30168	79	51	model	model	NOUN
ajst-30168	79	52	parameters	parameter	NOUN
ajst-30168	79	53	.	.	PUNCT
ajst-30168	80	1	the	the	DET
ajst-30168	80	2	optimization	optimization	NOUN
ajst-30168	80	3	targets	target	NOUN
ajst-30168	80	4	minimizing	minimize	VERB
ajst-30168	80	5	the	the	DET
ajst-30168	80	6	cross	cross	ADJ
ajst-30168	80	7	-	-	ADJ
ajst-30168	80	8	entropy	entropy	ADJ
ajst-30168	80	9	loss	loss	NOUN
ajst-30168	80	10	function	function	NOUN
ajst-30168	80	11	,	,	PUNCT
ajst-30168	80	12	where	where	SCONJ
ajst-30168	80	13	𝑦	𝑦	NOUN
ajst-30168	80	14	and	and	CCONJ
ajst-30168	80	15	𝑦	𝑦	NOUN
ajst-30168	80	16	correspond	correspond	VERB
ajst-30168	80	17	to	to	ADP
ajst-30168	80	18	the	the	DET
ajst-30168	80	19	actual	actual	ADJ
ajst-30168	80	20	and	and	CCONJ
ajst-30168	80	21	predicted	predict	VERB
ajst-30168	80	22	labels	label	NOUN
ajst-30168	80	23	for	for	ADP
ajst-30168	80	24	the	the	DET
ajst-30168	80	25	i	i	PROPN
ajst-30168	80	26	-	-	PUNCT
ajst-30168	80	27	th	th	X
ajst-30168	80	28	sample	sample	NOUN
ajst-30168	80	29	,	,	PUNCT
ajst-30168	80	30	respectively	respectively	ADV
ajst-30168	80	31	.	.	PUNCT
ajst-30168	81	1	the	the	DET
ajst-30168	81	2	batch	batch	NOUN
ajst-30168	81	3	size	size	NOUN
ajst-30168	81	4	of	of	ADP
ajst-30168	81	5	samples	sample	NOUN
ajst-30168	81	6	is	be	AUX
ajst-30168	81	7	denoted	denote	VERB
ajst-30168	81	8	by	by	ADP
ajst-30168	81	9	n	n	CCONJ
ajst-30168	81	10	,	,	PUNCT
ajst-30168	81	11	and	and	CCONJ
ajst-30168	81	12	𝜃	𝜃	PRON
ajst-30168	81	13	represents	represent	VERB
ajst-30168	81	14	the	the	DET
ajst-30168	81	15	trainable	trainable	ADJ
ajst-30168	81	16	parameters	parameter	NOUN
ajst-30168	81	17	during	during	ADP
ajst-30168	81	18	training	training	NOUN
ajst-30168	81	19	.	.	PUNCT
ajst-30168	82	1	furthermore	furthermore	ADV
ajst-30168	82	2	,	,	PUNCT
ajst-30168	82	3	we	we	PRON
ajst-30168	82	4	implement	implement	VERB
ajst-30168	82	5	an	an	DET
ajst-30168	82	6	early	early	ADJ
ajst-30168	82	7	stopping	stopping	NOUN
ajst-30168	82	8	mechanism	mechanism	NOUN
ajst-30168	82	9	to	to	PART
ajst-30168	82	10	prevent	prevent	VERB
ajst-30168	82	11	overfitting	overfitting	NOUN
ajst-30168	82	12	;	;	PUNCT
ajst-30168	82	13	training	training	NOUN
ajst-30168	82	14	is	be	AUX
ajst-30168	82	15	terminated	terminate	VERB
ajst-30168	82	16	if	if	SCONJ
ajst-30168	82	17	there	there	PRON
ajst-30168	82	18	is	be	VERB
ajst-30168	82	19	no	no	DET
ajst-30168	82	20	reduction	reduction	NOUN
ajst-30168	82	21	in	in	ADP
ajst-30168	82	22	the	the	DET
ajst-30168	82	23	validation	validation	NOUN
ajst-30168	82	24	loss	loss	NOUN
ajst-30168	82	25	for	for	ADP
ajst-30168	82	26	10	10	NUM
ajst-30168	82	27	successive	successive	ADJ
ajst-30168	82	28	epochs	epoch	NOUN
ajst-30168	82	29	.	.	PUNCT
ajst-30168	83	1	the	the	DET
ajst-30168	83	2	mini	mini	NOUN
ajst-30168	83	3	-	-	NOUN
ajst-30168	83	4	models	model	NOUN
ajst-30168	83	5	adopt	adopt	VERB
ajst-30168	83	6	a	a	DET
ajst-30168	83	7	training	training	NOUN
ajst-30168	83	8	strategy	strategy	NOUN
ajst-30168	83	9	in	in	ADP
ajst-30168	83	10	the	the	DET
ajst-30168	83	11	transfer	transfer	NOUN
ajst-30168	83	12	learning	learning	NOUN
ajst-30168	83	13	phase	phase	NOUN
ajst-30168	83	14	that	that	PRON
ajst-30168	83	15	closely	closely	ADV
ajst-30168	83	16	mirrors	mirror	VERB
ajst-30168	83	17	the	the	DET
ajst-30168	83	18	base	base	NOUN
ajst-30168	83	19	model	model	NOUN
ajst-30168	83	20	's	's	PART
ajst-30168	83	21	approach	approach	NOUN
ajst-30168	83	22	,	,	PUNCT
ajst-30168	83	23	with	with	ADP
ajst-30168	83	24	the	the	DET
ajst-30168	83	25	learning	learning	NOUN
ajst-30168	83	26	rate	rate	NOUN
ajst-30168	83	27	adjusted	adjust	VERB
ajst-30168	83	28	to	to	ADP
ajst-30168	83	29	0.0001	0.0001	NUM
ajst-30168	83	30	.	.	PUNCT
ajst-30168	84	1	5.2	5.2	NUM
ajst-30168	84	2	.	.	PUNCT
ajst-30168	85	1	model	model	NOUN
ajst-30168	85	2	evaluation	evaluation	NOUN
ajst-30168	85	3	we	we	PRON
ajst-30168	85	4	determined	determine	VERB
ajst-30168	85	5	the	the	DET
ajst-30168	85	6	accuracy	accuracy	NOUN
ajst-30168	85	7	(	(	PUNCT
ajst-30168	85	8	acc	acc	PROPN
ajst-30168	85	9	)	)	PUNCT
ajst-30168	85	10	and	and	CCONJ
ajst-30168	85	11	the	the	DET
ajst-30168	85	12	area	area	NOUN
ajst-30168	85	13	under	under	ADP
ajst-30168	85	14	the	the	DET
ajst-30168	85	15	roc	roc	PROPN
ajst-30168	85	16	curve	curve	NOUN
ajst-30168	85	17	(	(	PUNCT
ajst-30168	85	18	auc	auc	NOUN
ajst-30168	85	19	)	)	PUNCT
ajst-30168	85	20	using	use	VERB
ajst-30168	85	21	actual	actual	ADJ
ajst-30168	85	22	and	and	CCONJ
ajst-30168	85	23	predicted	predict	VERB
ajst-30168	85	24	labels	label	NOUN
ajst-30168	85	25	.	.	PUNCT
ajst-30168	86	1	figure	figure	NOUN
ajst-30168	86	2	x	x	PRON
ajst-30168	86	3	illustrates	illustrate	VERB
ajst-30168	86	4	that	that	SCONJ
ajst-30168	86	5	93	93	NUM
ajst-30168	86	6	%	%	NOUN
ajst-30168	86	7	of	of	ADP
ajst-30168	86	8	the	the	DET
ajst-30168	86	9	mini	mini	NOUN
ajst-30168	86	10	-	-	NOUN
ajst-30168	86	11	models	model	NOUN
ajst-30168	86	12	obtained	obtain	VERB
ajst-30168	86	13	an	an	DET
ajst-30168	86	14	auc	auc	NOUN
ajst-30168	86	15	above	above	ADP
ajst-30168	86	16	0.95	0.95	NUM
ajst-30168	86	17	,	,	PUNCT
ajst-30168	86	18	with	with	ADP
ajst-30168	86	19	even	even	ADV
ajst-30168	86	20	the	the	PRON
ajst-30168	86	21	least	least	ADV
ajst-30168	86	22	accurate	accurate	ADJ
ajst-30168	86	23	mini	mini	NOUN
ajst-30168	86	24	-	-	NOUN
ajst-30168	86	25	model	model	NOUN
ajst-30168	86	26	attaining	attain	VERB
ajst-30168	86	27	a	a	DET
ajst-30168	86	28	precision	precision	NOUN
ajst-30168	86	29	rate	rate	NOUN
ajst-30168	86	30	of	of	ADP
ajst-30168	86	31	0.901(figure	0.901(figure	NUM
ajst-30168	86	32	5a	5a	NUM
ajst-30168	86	33	)	)	PUNCT
ajst-30168	86	34	.	.	PUNCT
ajst-30168	87	1	we	we	PRON
ajst-30168	87	2	conducted	conduct	VERB
ajst-30168	87	3	validation	validation	NOUN
ajst-30168	87	4	of	of	ADP
ajst-30168	87	5	the	the	DET
ajst-30168	87	6	mini	mini	NOUN
ajst-30168	87	7	-	-	NOUN
ajst-30168	87	8	models	model	NOUN
ajst-30168	87	9	using	use	VERB
ajst-30168	87	10	a	a	DET
ajst-30168	87	11	publicly	publicly	ADV
ajst-30168	87	12	available	available	ADJ
ajst-30168	87	13	dataset	dataset	NOUN
ajst-30168	87	14	[	[	PUNCT
ajst-30168	87	15	11	11	NUM
ajst-30168	87	16	]	]	PUNCT
ajst-30168	87	17	.	.	PUNCT
ajst-30168	88	1	the	the	DET
ajst-30168	88	2	predictive	predictive	ADJ
ajst-30168	88	3	outcomes	outcome	NOUN
ajst-30168	88	4	for	for	ADP
ajst-30168	88	5	both	both	CCONJ
ajst-30168	88	6	negative	negative	ADJ
ajst-30168	88	7	and	and	CCONJ
ajst-30168	88	8	positive	positive	ADJ
ajst-30168	88	9	sites	site	NOUN
ajst-30168	88	10	are	be	AUX
ajst-30168	88	11	depicted	depict	VERB
ajst-30168	88	12	in	in	ADP
ajst-30168	88	13	figure	figure	NOUN
ajst-30168	88	14	5b	5b	NUM
ajst-30168	88	15	.	.	PUNCT
ajst-30168	89	1	6	6	NUM
ajst-30168	89	2	.	.	X
ajst-30168	89	3	conclusion	conclusion	NOUN
ajst-30168	89	4	in	in	ADP
ajst-30168	89	5	this	this	DET
ajst-30168	89	6	study	study	NOUN
ajst-30168	89	7	,	,	PUNCT
ajst-30168	89	8	we	we	PRON
ajst-30168	89	9	present	present	VERB
ajst-30168	89	10	a	a	DET
ajst-30168	89	11	novel	novel	ADJ
ajst-30168	89	12	single	single	ADJ
ajst-30168	89	13	-	-	PUNCT
ajst-30168	89	14	motif	motif	NOUN
ajst-30168	89	15	model	model	NOUN
ajst-30168	89	16	termed	term	VERB
ajst-30168	89	17	"	"	PUNCT
ajst-30168	89	18	single	single	ADJ
ajst-30168	89	19	-	-	PUNCT
ajst-30168	89	20	nano	nano	NOUN
ajst-30168	89	21	,	,	PUNCT
ajst-30168	89	22	"	"	PUNCT
ajst-30168	89	23	which	which	PRON
ajst-30168	89	24	integrates	integrate	VERB
ajst-30168	89	25	1d	1d	NUM
ajst-30168	89	26	convolutional	convolutional	ADJ
ajst-30168	89	27	neural	neural	ADJ
ajst-30168	89	28	networks	network	NOUN
ajst-30168	89	29	(	(	PUNCT
ajst-30168	89	30	1dcnn	1dcnn	NUM
ajst-30168	89	31	)	)	PUNCT
ajst-30168	89	32	and	and	CCONJ
ajst-30168	89	33	bidirectional	bidirectional	ADJ
ajst-30168	89	34	long	long	ADJ
ajst-30168	89	35	short	short	ADJ
ajst-30168	89	36	-	-	PUNCT
ajst-30168	89	37	term	term	NOUN
ajst-30168	89	38	memory	memory	NOUN
ajst-30168	89	39	(	(	PUNCT
ajst-30168	89	40	bilstm	bilstm	NOUN
ajst-30168	89	41	)	)	PUNCT
ajst-30168	89	42	architectures	architecture	NOUN
ajst-30168	89	43	for	for	ADP
ajst-30168	89	44	the	the	DET
ajst-30168	89	45	detection	detection	NOUN
ajst-30168	89	46	of	of	ADP
ajst-30168	89	47	cpg	cpg	NOUN
ajst-30168	89	48	methylation	methylation	NOUN
ajst-30168	89	49	in	in	ADP
ajst-30168	89	50	dna	dna	PROPN
ajst-30168	89	51	sequences	sequence	NOUN
ajst-30168	89	52	.	.	PUNCT
ajst-30168	90	1	this	this	DET
ajst-30168	90	2	model	model	NOUN
ajst-30168	90	3	innovatively	innovatively	ADV
ajst-30168	90	4	segments	segment	VERB
ajst-30168	90	5	the	the	DET
ajst-30168	90	6	traditional	traditional	ADJ
ajst-30168	90	7	5mc	5mc	ADJ
ajst-30168	90	8	methylation	methylation	NOUN
ajst-30168	90	9	detection	detection	NOUN
ajst-30168	90	10	task	task	NOUN
ajst-30168	90	11	based	base	VERB
ajst-30168	90	12	on	on	ADP
ajst-30168	90	13	motifs	motif	NOUN
ajst-30168	90	14	,	,	PUNCT
ajst-30168	90	15	thereby	thereby	ADV
ajst-30168	90	16	overcoming	overcome	VERB
ajst-30168	90	17	the	the	DET
ajst-30168	90	18	limitations	limitation	NOUN
ajst-30168	90	19	of	of	ADP
ajst-30168	90	20	conventional	conventional	ADJ
ajst-30168	90	21	models	model	NOUN
ajst-30168	90	22	that	that	PRON
ajst-30168	90	23	struggle	struggle	VERB
ajst-30168	90	24	with	with	ADP
ajst-30168	90	25	accurate	accurate	ADJ
ajst-30168	90	26	identification	identification	NOUN
ajst-30168	90	27	on	on	ADP
ajst-30168	90	28	specific	specific	ADJ
ajst-30168	90	29	motifs	motif	NOUN
ajst-30168	90	30	.	.	PUNCT
ajst-30168	91	1	as	as	ADP
ajst-30168	91	2	a	a	DET
ajst-30168	91	3	result	result	NOUN
ajst-30168	91	4	,	,	PUNCT
ajst-30168	91	5	single	single	ADJ
ajst-30168	91	6	-	-	PUNCT
ajst-30168	91	7	nano	nano	NOUN
ajst-30168	91	8	enhances	enhance	VERB
ajst-30168	91	9	the	the	DET
ajst-30168	91	10	accuracy	accuracy	NOUN
ajst-30168	91	11	of	of	ADP
ajst-30168	91	12	methylation	methylation	NOUN
ajst-30168	91	13	detection	detection	NOUN
ajst-30168	91	14	.	.	PUNCT
ajst-30168	92	1	the	the	DET
ajst-30168	92	2	natural	natural	ADJ
ajst-30168	92	3	world	world	NOUN
ajst-30168	92	4	is	be	AUX
ajst-30168	92	5	replete	replete	ADJ
ajst-30168	92	6	with	with	ADP
ajst-30168	92	7	various	various	ADJ
ajst-30168	92	8	dna	dna	NOUN
ajst-30168	92	9	and	and	CCONJ
ajst-30168	92	10	rna	rna	NOUN
ajst-30168	92	11	modifications	modification	NOUN
ajst-30168	92	12	,	,	PUNCT
ajst-30168	92	13	many	many	ADJ
ajst-30168	92	14	of	of	ADP
ajst-30168	92	15	which	which	PRON
ajst-30168	92	16	present	present	ADJ
ajst-30168	92	17	significant	significant	ADJ
ajst-30168	92	18	challenges	challenge	NOUN
ajst-30168	92	19	in	in	ADP
ajst-30168	92	20	detection	detection	NOUN
ajst-30168	92	21	.	.	PUNCT
ajst-30168	93	1	single	single	ADJ
ajst-30168	93	2	-	-	PUNCT
ajst-30168	93	3	nano	nano	NOUN
ajst-30168	93	4	stands	stand	VERB
ajst-30168	93	5	out	out	ADP
ajst-30168	93	6	as	as	ADP
ajst-30168	93	7	a	a	DET
ajst-30168	93	8	versatile	versatile	ADJ
ajst-30168	93	9	model	model	NOUN
ajst-30168	93	10	capable	capable	ADJ
ajst-30168	93	11	of	of	ADP
ajst-30168	93	12	training	training	NOUN
ajst-30168	93	13	on	on	ADP
ajst-30168	93	14	individual	individual	ADJ
ajst-30168	93	15	motifs	motif	NOUN
ajst-30168	93	16	,	,	PUNCT
ajst-30168	93	17	even	even	ADV
ajst-30168	93	18	those	those	PRON
ajst-30168	93	19	that	that	PRON
ajst-30168	93	20	are	be	AUX
ajst-30168	93	21	rare	rare	ADJ
ajst-30168	93	22	.	.	PUNCT
ajst-30168	94	1	moreover	moreover	ADV
ajst-30168	94	2	,	,	PUNCT
ajst-30168	94	3	it	it	PRON
ajst-30168	94	4	offers	offer	VERB
ajst-30168	94	5	the	the	DET
ajst-30168	94	6	potential	potential	NOUN
ajst-30168	94	7	for	for	ADP
ajst-30168	94	8	transfer	transfer	NOUN
ajst-30168	94	9	learning	learning	NOUN
ajst-30168	94	10	across	across	ADP
ajst-30168	94	11	different	different	ADJ
ajst-30168	94	12	types	type	NOUN
ajst-30168	94	13	of	of	ADP
ajst-30168	94	14	modifications	modification	NOUN
ajst-30168	94	15	.	.	PUNCT
ajst-30168	95	1	in	in	ADP
ajst-30168	95	2	conclusion	conclusion	NOUN
ajst-30168	95	3	,	,	PUNCT
ajst-30168	95	4	single	single	ADJ
ajst-30168	95	5	-	-	PUNCT
ajst-30168	95	6	nano	nano	NOUN
ajst-30168	95	7	is	be	AUX
ajst-30168	95	8	well	well	ADV
ajst-30168	95	9	-	-	PUNCT
ajst-30168	95	10	suited	suited	ADJ
ajst-30168	95	11	for	for	ADP
ajst-30168	95	12	detecting	detect	VERB
ajst-30168	95	13	a	a	DET
ajst-30168	95	14	variety	variety	NOUN
ajst-30168	95	15	of	of	ADP
ajst-30168	95	16	modifications	modification	NOUN
ajst-30168	95	17	on	on	ADP
ajst-30168	95	18	nanopores	nanopore	NOUN
ajst-30168	95	19	.	.	PUNCT
ajst-30168	96	1	its	its	PRON
ajst-30168	96	2	motif	motif	NOUN
ajst-30168	96	3	-	-	PUNCT
ajst-30168	96	4	based	base	VERB
ajst-30168	96	5	segmentation	segmentation	NOUN
ajst-30168	96	6	approach	approach	NOUN
ajst-30168	96	7	and	and	CCONJ
ajst-30168	96	8	adaptability	adaptability	NOUN
ajst-30168	96	9	to	to	PART
ajst-30168	96	10	diverse	diverse	ADJ
ajst-30168	96	11	modifications	modification	NOUN
ajst-30168	96	12	render	render	VERB
ajst-30168	96	13	it	it	PRON
ajst-30168	96	14	a	a	DET
ajst-30168	96	15	valuable	valuable	ADJ
ajst-30168	96	16	asset	asset	NOUN
ajst-30168	96	17	for	for	ADP
ajst-30168	96	18	methylation	methylation	NOUN
ajst-30168	96	19	detection	detection	NOUN
ajst-30168	96	20	and	and	CCONJ
ajst-30168	96	21	potentially	potentially	ADV
ajst-30168	96	22	broader	broad	ADJ
ajst-30168	96	23	applications	application	NOUN
ajst-30168	96	24	in	in	ADP
ajst-30168	96	25	epigenetic	epigenetic	ADJ
ajst-30168	96	26	research	research	NOUN
ajst-30168	96	27	.	.	PUNCT
ajst-30168	97	1	21	21	NUM
ajst-30168	97	2	references	reference	NOUN
ajst-30168	97	3	[	[	X
ajst-30168	97	4	1	1	NUM
ajst-30168	97	5	]	]	X
ajst-30168	97	6	woods	woods	PROPN
ajst-30168	97	7	d.	d.	PROPN
ajst-30168	97	8	,	,	PUNCT
ajst-30168	97	9	doty	doty	PROPN
ajst-30168	97	10	d.	d.	PROPN
ajst-30168	97	11	,	,	PUNCT
ajst-30168	97	12	myhrvold	myhrvold	PROPN
ajst-30168	97	13	c.	c.	PROPN
ajst-30168	97	14	,	,	PUNCT
ajst-30168	97	15	et	et	PROPN
ajst-30168	97	16	al	al	PROPN
ajst-30168	97	17	.	.	PROPN
ajst-30168	97	18	diverse	diverse	ADJ
ajst-30168	97	19	and	and	CCONJ
ajst-30168	97	20	robust	robust	ADJ
ajst-30168	97	21	molecular	molecular	ADJ
ajst-30168	97	22	algorithms	algorithm	NOUN
ajst-30168	97	23	using	use	VERB
ajst-30168	97	24	reprogrammable	reprogrammable	ADJ
ajst-30168	97	25	dna	dna	PROPN
ajst-30168	97	26	selfassembly[j	selfassembly[j	PROPN
ajst-30168	97	27	]	]	PUNCT
ajst-30168	97	28	.	.	PUNCT
ajst-30168	98	1	nature	nature	NOUN
ajst-30168	98	2	567	567	NUM
ajst-30168	98	3	,	,	PUNCT
ajst-30168	98	4	2019	2019	NUM
ajst-30168	98	5	,	,	PUNCT
ajst-30168	98	6	366	366	NUM
ajst-30168	98	7	-	-	SYM
ajst-30168	98	8	372	372	NUM
ajst-30168	98	9	[	[	X
ajst-30168	98	10	2	2	NUM
ajst-30168	98	11	]	]	X
ajst-30168	98	12	zhang	zhang	PROPN
ajst-30168	98	13	d.	d.	PROPN
ajst-30168	98	14	,	,	PUNCT
ajst-30168	98	15	hariadi	hariadi	PROPN
ajst-30168	98	16	r.	r.	PROPN
ajst-30168	98	17	,	,	PUNCT
ajst-30168	98	18	choi	choi	PROPN
ajst-30168	98	19	h.	h.	PROPN
ajst-30168	98	20	,	,	PUNCT
ajst-30168	98	21	et	et	PROPN
ajst-30168	98	22	al	al	PROPN
ajst-30168	98	23	.	.	PUNCT
ajst-30168	98	24	integrating	integrate	VERB
ajst-30168	98	25	dna	dna	PROPN
ajst-30168	98	26	stranddisplacement	stranddisplacement	NOUN
ajst-30168	98	27	circuitry	circuitry	NOUN
ajst-30168	98	28	with	with	ADP
ajst-30168	98	29	dna	dna	PROPN
ajst-30168	98	30	tile	tile	NOUN
ajst-30168	98	31	self	self	NOUN
ajst-30168	98	32	-	-	PUNCT
ajst-30168	98	33	assembly[j	assembly[j	NOUN
ajst-30168	98	34	]	]	PUNCT
ajst-30168	98	35	.	.	PUNCT
ajst-30168	99	1	nature	nature	NOUN
ajst-30168	99	2	communications	communication	NOUN
ajst-30168	99	3	4	4	NUM
ajst-30168	99	4	,	,	PUNCT
ajst-30168	99	5	2013	2013	NUM
ajst-30168	99	6	,	,	PUNCT
ajst-30168	99	7	1965	1965	NUM
ajst-30168	100	1	[	[	X
ajst-30168	100	2	3	3	NUM
ajst-30168	100	3	]	]	X
ajst-30168	100	4	song	song	NOUN
ajst-30168	100	5	t.	t.	PROPN
ajst-30168	100	6	,	,	PUNCT
ajst-30168	100	7	eshra	eshra	PROPN
ajst-30168	100	8	a.	a.	PROPN
ajst-30168	100	9	,	,	PUNCT
ajst-30168	100	10	shah	shah	PROPN
ajst-30168	100	11	s.	s.	PROPN
ajst-30168	100	12	,	,	PUNCT
ajst-30168	100	13	et	et	PROPN
ajst-30168	100	14	al	al	PROPN
ajst-30168	100	15	.	.	PROPN
ajst-30168	100	16	fast	fast	ADJ
ajst-30168	100	17	and	and	CCONJ
ajst-30168	100	18	compact	compact	ADJ
ajst-30168	100	19	dna	dna	NOUN
ajst-30168	100	20	logic	logic	NOUN
ajst-30168	100	21	circuits	circuit	NOUN
ajst-30168	100	22	based	base	VERB
ajst-30168	100	23	on	on	ADP
ajst-30168	100	24	single	single	ADJ
ajst-30168	100	25	-	-	PUNCT
ajst-30168	100	26	stranded	strand	VERB
ajst-30168	100	27	gates	gate	NOUN
ajst-30168	100	28	using	use	VERB
ajst-30168	100	29	strand	strand	NOUN
ajst-30168	100	30	-	-	PUNCT
ajst-30168	100	31	displacing	displace	VERB
ajst-30168	100	32	polymerase[j	polymerase[j	NOUN
ajst-30168	100	33	]	]	PUNCT
ajst-30168	100	34	.	.	PUNCT
ajst-30168	101	1	nature	nature	PROPN
ajst-30168	101	2	nanotechnology	nanotechnology	PROPN
ajst-30168	101	3	14	14	NUM
ajst-30168	101	4	,	,	PUNCT
ajst-30168	101	5	2019	2019	NUM
ajst-30168	101	6	,	,	PUNCT
ajst-30168	101	7	1075	1075	NUM
ajst-30168	101	8	-	-	SYM
ajst-30168	101	9	1081	1081	NUM
ajst-30168	101	10	[	[	X
ajst-30168	101	11	4	4	NUM
ajst-30168	101	12	]	]	X
ajst-30168	101	13	frommer	frommer	NOUN
ajst-30168	101	14	,	,	PUNCT
ajst-30168	101	15	m.	m.	NOUN
ajst-30168	101	16	et	et	PROPN
ajst-30168	101	17	al	al	PROPN
ajst-30168	101	18	.	.	PUNCT
ajst-30168	102	1	a	a	DET
ajst-30168	102	2	genomic	genomic	NOUN
ajst-30168	102	3	sequencing	sequence	VERB
ajst-30168	102	4	protocol	protocol	NOUN
ajst-30168	102	5	that	that	PRON
ajst-30168	102	6	yields	yield	VERB
ajst-30168	102	7	a	a	DET
ajst-30168	102	8	positive	positive	ADJ
ajst-30168	102	9	display	display	NOUN
ajst-30168	102	10	of	of	ADP
ajst-30168	102	11	5	5	NUM
ajst-30168	102	12	-	-	PUNCT
ajst-30168	102	13	methylcytosine	methylcytosine	NOUN
ajst-30168	102	14	residues	residue	NOUN
ajst-30168	102	15	in	in	ADP
ajst-30168	102	16	individual	individual	ADJ
ajst-30168	102	17	dna	dna	PROPN
ajst-30168	102	18	strands	strand	NOUN
ajst-30168	102	19	.	.	PUNCT
ajst-30168	103	1	proc	proc	PROPN
ajst-30168	103	2	.	.	PUNCT
ajst-30168	104	1	natl	natl	PROPN
ajst-30168	104	2	acad	acad	PROPN
ajst-30168	104	3	.	.	PUNCT
ajst-30168	105	1	sci	sci	PROPN
ajst-30168	105	2	.	.	PROPN
ajst-30168	105	3	usa	usa	PROPN
ajst-30168	105	4	89	89	NUM
ajst-30168	105	5	,	,	PUNCT
ajst-30168	105	6	1827–1831	1827–1831	NUM
ajst-30168	105	7	(	(	PUNCT
ajst-30168	105	8	1992	1992	NUM
ajst-30168	105	9	)	)	PUNCT
ajst-30168	105	10	.	.	PUNCT
ajst-30168	106	1	[	[	X
ajst-30168	106	2	5	5	NUM
ajst-30168	106	3	]	]	SYM
ajst-30168	106	4	simpson	simpson	PROPN
ajst-30168	106	5	,	,	PUNCT
ajst-30168	106	6	j.	j.	PROPN
ajst-30168	106	7	t.	t.	PROPN
ajst-30168	106	8	et	et	PROPN
ajst-30168	106	9	al	al	PROPN
ajst-30168	106	10	.	.	PUNCT
ajst-30168	107	1	detecting	detect	VERB
ajst-30168	107	2	dna	dna	PROPN
ajst-30168	107	3	cytosine	cytosine	NOUN
ajst-30168	107	4	methylation	methylation	NOUN
ajst-30168	107	5	using	use	VERB
ajst-30168	107	6	nanopore	nanopore	ADJ
ajst-30168	107	7	sequencing	sequencing	NOUN
ajst-30168	107	8	.	.	PUNCT
ajst-30168	108	1	nat	nat	PROPN
ajst-30168	108	2	.	.	PUNCT
ajst-30168	109	1	methods	method	NOUN
ajst-30168	109	2	14	14	NUM
ajst-30168	109	3	,	,	PUNCT
ajst-30168	109	4	407–410	407–410	NUM
ajst-30168	109	5	(	(	PUNCT
ajst-30168	109	6	2017	2017	NUM
ajst-30168	109	7	)	)	PUNCT
ajst-30168	109	8	.	.	PUNCT
ajst-30168	110	1	[	[	X
ajst-30168	110	2	6	6	NUM
ajst-30168	110	3	]	]	SYM
ajst-30168	110	4	ni	ni	PROPN
ajst-30168	110	5	,	,	PUNCT
ajst-30168	110	6	p.	p.	NOUN
ajst-30168	110	7	et	et	NOUN
ajst-30168	111	1	al	al	PROPN
ajst-30168	111	2	.	.	PROPN
ajst-30168	111	3	deepsignal	deepsignal	PROPN
ajst-30168	111	4	:	:	PUNCT
ajst-30168	111	5	detecting	detect	VERB
ajst-30168	111	6	dna	dna	PROPN
ajst-30168	111	7	methylation	methylation	NOUN
ajst-30168	111	8	state	state	NOUN
ajst-30168	111	9	from	from	ADP
ajst-30168	111	10	nanopore	nanopore	ADJ
ajst-30168	111	11	sequencing	sequence	VERB
ajst-30168	111	12	reads	read	NOUN
ajst-30168	111	13	using	use	VERB
ajst-30168	111	14	deep	deep	ADJ
ajst-30168	111	15	-	-	PUNCT
ajst-30168	111	16	learning	learning	NOUN
ajst-30168	111	17	.	.	PUNCT
ajst-30168	112	1	bioinformatics	bioinformatic	NOUN
ajst-30168	112	2	35	35	NUM
ajst-30168	112	3	,	,	PUNCT
ajst-30168	112	4	4586–4595	4586–4595	NUM
ajst-30168	112	5	(	(	PUNCT
ajst-30168	112	6	2019	2019	NUM
ajst-30168	112	7	)	)	PUNCT
ajst-30168	112	8	.	.	PUNCT
ajst-30168	113	1	[	[	X
ajst-30168	113	2	7	7	X
ajst-30168	113	3	]	]	X
ajst-30168	113	4	liu	liu	PROPN
ajst-30168	113	5	,	,	PUNCT
ajst-30168	113	6	q.	q.	PROPN
ajst-30168	113	7	et	et	PROPN
ajst-30168	113	8	al	al	PROPN
ajst-30168	113	9	.	.	PUNCT
ajst-30168	113	10	detection	detection	NOUN
ajst-30168	113	11	of	of	ADP
ajst-30168	113	12	dna	dna	PROPN
ajst-30168	113	13	base	base	NOUN
ajst-30168	113	14	modifications	modification	NOUN
ajst-30168	113	15	by	by	ADP
ajst-30168	113	16	deep	deep	ADJ
ajst-30168	113	17	recurrent	recurrent	ADJ
ajst-30168	113	18	neural	neural	ADJ
ajst-30168	113	19	network	network	NOUN
ajst-30168	113	20	on	on	ADP
ajst-30168	113	21	oxford	oxford	PROPN
ajst-30168	113	22	nanopore	nanopore	ADV
ajst-30168	113	23	sequencing	sequence	VERB
ajst-30168	113	24	data	datum	NOUN
ajst-30168	113	25	.	.	PUNCT
ajst-30168	114	1	nat	nat	PROPN
ajst-30168	114	2	.	.	PUNCT
ajst-30168	115	1	commun	commun	PROPN
ajst-30168	115	2	.	.	PUNCT
ajst-30168	116	1	10	10	NUM
ajst-30168	116	2	,	,	PUNCT
ajst-30168	116	3	2449	2449	NUM
ajst-30168	116	4	(	(	PUNCT
ajst-30168	116	5	2019	2019	NUM
ajst-30168	116	6	)	)	PUNCT
ajst-30168	116	7	.	.	PUNCT
ajst-30168	117	1	[	[	X
ajst-30168	117	2	8	8	NUM
ajst-30168	117	3	]	]	X
ajst-30168	117	4	stanojević	stanojević	ADJ
ajst-30168	117	5	,	,	PUNCT
ajst-30168	117	6	d.	d.	PROPN
ajst-30168	117	7	,	,	PUNCT
ajst-30168	117	8	li	li	PROPN
ajst-30168	117	9	,	,	PUNCT
ajst-30168	117	10	z.	z.	PROPN
ajst-30168	117	11	,	,	PUNCT
ajst-30168	117	12	bakić	bakić	PROPN
ajst-30168	117	13	,	,	PUNCT
ajst-30168	117	14	s.	s.	PROPN
ajst-30168	117	15	et	et	PROPN
ajst-30168	117	16	al	al	PROPN
ajst-30168	117	17	.	.	PROPN
ajst-30168	117	18	rockfish	rockfish	PROPN
ajst-30168	117	19	:	:	PUNCT
ajst-30168	117	20	a	a	DET
ajst-30168	117	21	transformerbased	transformerbase	VERB
ajst-30168	117	22	model	model	NOUN
ajst-30168	117	23	for	for	ADP
ajst-30168	117	24	accurate	accurate	ADJ
ajst-30168	117	25	5	5	NUM
ajst-30168	117	26	-	-	PUNCT
ajst-30168	117	27	methylcytosine	methylcytosine	NOUN
ajst-30168	117	28	prediction	prediction	NOUN
ajst-30168	117	29	from	from	ADP
ajst-30168	117	30	nanopore	nanopore	ADJ
ajst-30168	117	31	sequencing	sequencing	NOUN
ajst-30168	117	32	.	.	PUNCT
ajst-30168	118	1	nat	nat	PROPN
ajst-30168	118	2	commun	commun	PROPN
ajst-30168	118	3	15	15	NUM
ajst-30168	118	4	,	,	PUNCT
ajst-30168	118	5	5580	5580	NUM
ajst-30168	118	6	(	(	PUNCT
ajst-30168	118	7	2024	2024	NUM
ajst-30168	118	8	)	)	PUNCT
ajst-30168	118	9	.	.	PUNCT
ajst-30168	119	1	[	[	X
ajst-30168	119	2	9	9	NUM
ajst-30168	119	3	]	]	SYM
ajst-30168	119	4	yuen	yuen	PROPN
ajst-30168	119	5	,	,	PUNCT
ajst-30168	119	6	z.ws	z.ws	PROPN
ajst-30168	119	7	.	.	PROPN
ajst-30168	119	8	,	,	PUNCT
ajst-30168	119	9	srivastava	srivastava	PROPN
ajst-30168	119	10	,	,	PUNCT
ajst-30168	119	11	a.	a.	PROPN
ajst-30168	119	12	,	,	PUNCT
ajst-30168	119	13	daniel	daniel	PROPN
ajst-30168	119	14	,	,	PUNCT
ajst-30168	119	15	r.	r.	PROPN
ajst-30168	119	16	et	et	PROPN
ajst-30168	119	17	al	al	PROPN
ajst-30168	119	18	.	.	PUNCT
ajst-30168	120	1	systematic	systematic	ADJ
ajst-30168	120	2	benchmarking	benchmarking	NOUN
ajst-30168	120	3	of	of	ADP
ajst-30168	120	4	tools	tool	NOUN
ajst-30168	120	5	for	for	ADP
ajst-30168	120	6	cpg	cpg	NOUN
ajst-30168	120	7	methylation	methylation	NOUN
ajst-30168	120	8	detection	detection	NOUN
ajst-30168	120	9	from	from	ADP
ajst-30168	120	10	nanopore	nanopore	ADJ
ajst-30168	120	11	sequencing	sequencing	NOUN
ajst-30168	120	12	.	.	PUNCT
ajst-30168	121	1	nat	nat	PROPN
ajst-30168	121	2	commun	commun	PROPN
ajst-30168	121	3	12	12	NUM
ajst-30168	121	4	,	,	PUNCT
ajst-30168	121	5	3438	3438	NUM
ajst-30168	121	6	(	(	PUNCT
ajst-30168	121	7	2021	2021	NUM
ajst-30168	121	8	)	)	PUNCT
ajst-30168	121	9	.	.	PUNCT
ajst-30168	122	1	[	[	X
ajst-30168	122	2	10	10	NUM
ajst-30168	122	3	]	]	X
ajst-30168	122	4	jain	jain	NOUN
ajst-30168	122	5	,	,	PUNCT
ajst-30168	122	6	m.	m.	NOUN
ajst-30168	122	7	,	,	PUNCT
ajst-30168	122	8	koren	koren	PROPN
ajst-30168	122	9	,	,	PUNCT
ajst-30168	122	10	s.	s.	PROPN
ajst-30168	122	11	,	,	PUNCT
ajst-30168	122	12	miga	miga	PROPN
ajst-30168	122	13	,	,	PUNCT
ajst-30168	122	14	k.	k.	PROPN
ajst-30168	122	15	et	et	PROPN
ajst-30168	122	16	al	al	PROPN
ajst-30168	122	17	.	.	PUNCT
ajst-30168	123	1	nanopore	nanopore	ADJ
ajst-30168	123	2	sequencing	sequencing	NOUN
ajst-30168	123	3	and	and	CCONJ
ajst-30168	123	4	assembly	assembly	NOUN
ajst-30168	123	5	of	of	ADP
ajst-30168	123	6	a	a	DET
ajst-30168	123	7	human	human	ADJ
ajst-30168	123	8	genome	genome	NOUN
ajst-30168	123	9	with	with	ADP
ajst-30168	123	10	ultra	ultra	ADJ
ajst-30168	123	11	-	-	ADJ
ajst-30168	123	12	long	long	ADJ
ajst-30168	123	13	reads	read	NOUN
ajst-30168	123	14	.	.	PUNCT
ajst-30168	124	1	nat	nat	PROPN
ajst-30168	124	2	biotechnol	biotechnol	PROPN
ajst-30168	124	3	36	36	NUM
ajst-30168	124	4	,	,	PUNCT
ajst-30168	124	5	338–345	338–345	NUM
ajst-30168	124	6	(	(	PUNCT
ajst-30168	124	7	2018	2018	NUM
ajst-30168	124	8	)	)	PUNCT
ajst-30168	124	9	.	.	PUNCT
ajst-30168	125	1	[	[	X
ajst-30168	125	2	11	11	NUM
ajst-30168	125	3	]	]	X
ajst-30168	125	4	zhang	zhang	PROPN
ajst-30168	125	5	,	,	PUNCT
ajst-30168	125	6	c.	c.	PROPN
ajst-30168	125	7	,	,	PUNCT
ajst-30168	125	8	wu	wu	PROPN
ajst-30168	125	9	,	,	PUNCT
ajst-30168	125	10	r.	r.	PROPN
ajst-30168	125	11	,	,	PUNCT
ajst-30168	125	12	sun	sun	PROPN
ajst-30168	125	13	,	,	PUNCT
ajst-30168	125	14	f.	f.	PROPN
ajst-30168	125	15	et	et	PROPN
ajst-30168	125	16	al	al	PROPN
ajst-30168	125	17	.	.	PROPN
ajst-30168	125	18	parallel	parallel	ADJ
ajst-30168	125	19	molecular	molecular	ADJ
ajst-30168	125	20	data	datum	NOUN
ajst-30168	125	21	storage	storage	NOUN
ajst-30168	125	22	by	by	ADP
ajst-30168	125	23	printing	print	VERB
ajst-30168	125	24	epigenetic	epigenetic	ADJ
ajst-30168	125	25	bits	bit	NOUN
ajst-30168	125	26	on	on	ADP
ajst-30168	125	27	dna	dna	PROPN
ajst-30168	125	28	.	.	PUNCT
ajst-30168	126	1	nature	nature	NOUN
ajst-30168	126	2	634	634	NUM
ajst-30168	126	3	,	,	PUNCT
ajst-30168	126	4	824–832	824–832	NUM
ajst-30168	126	5	(	(	PUNCT
ajst-30168	126	6	2024	2024	NUM
ajst-30168	126	7	)	)	PUNCT
ajst-30168	126	8	.	.	PUNCT