id	sid	tid	token	lemma	pos
gra-920	1	1	1.introduction	1.introduction	NUM
gra-920	1	2	in	in	ADP
gra-920	1	3	recent	recent	ADJ
gra-920	1	4	years	year	NOUN
gra-920	1	5	,	,	PUNCT
gra-920	1	6	low	low	ADJ
gra-920	1	7	-	-	PUNCT
gra-920	1	8	cost	cost	NOUN
gra-920	1	9	,	,	PUNCT
gra-920	1	10	high	high	ADJ
gra-920	1	11	-	-	PUNCT
gra-920	1	12	throughput	throughput	NOUN
gra-920	1	13	,	,	PUNCT
gra-920	1	14	digital	digital	ADJ
gra-920	1	15	rna	rna	NOUN
gra-920	1	16	-	-	PUNCT
gra-920	1	17	seq	seq	NOUN
gra-920	1	18	technology	technology	NOUN
gra-920	1	19	has	have	AUX
gra-920	1	20	gradually	gradually	ADV
gra-920	1	21	replaced	replace	VERB
gra-920	1	22	traditional	traditional	ADJ
gra-920	1	23	gene	gene	NOUN
gra-920	1	24	chip	chip	NOUN
gra-920	1	25	technology	technology	NOUN
gra-920	1	26	,	,	PUNCT
gra-920	1	27	and	and	CCONJ
gra-920	1	28	its	its	PRON
gra-920	1	29	widespread	widespread	ADJ
gra-920	1	30	application	application	NOUN
gra-920	1	31	has	have	AUX
gra-920	1	32	played	play	VERB
gra-920	1	33	an	an	DET
gra-920	1	34	important	important	ADJ
gra-920	1	35	role	role	NOUN
gra-920	1	36	in	in	ADP
gra-920	1	37	genomics	genomic	NOUN
gra-920	1	38	and	and	CCONJ
gra-920	1	39	gene	gene	NOUN
gra-920	1	40	expression	expression	NOUN
gra-920	2	1	[	[	X
gra-920	2	2	1	1	NUM
gra-920	2	3	]	]	PUNCT
gra-920	2	4	.	.	PUNCT
gra-920	3	1	the	the	DET
gra-920	3	2	rnaseq	rnaseq	NOUN
gra-920	3	3	technique	technique	NOUN
gra-920	3	4	is	be	AUX
gra-920	3	5	mainly	mainly	ADV
gra-920	3	6	used	use	VERB
gra-920	3	7	for	for	ADP
gra-920	3	8	sequencing	sequence	VERB
gra-920	3	9	the	the	DET
gra-920	3	10	cdna	cdna	PROPN
gra-920	3	11	fragments	fragment	NOUN
gra-920	3	12	which	which	PRON
gra-920	3	13	are	be	AUX
gra-920	3	14	reversely	reversely	ADV
gra-920	3	15	transcribed	transcribe	VERB
gra-920	3	16	by	by	ADP
gra-920	3	17	the	the	DET
gra-920	3	18	mrna	mrna	PROPN
gra-920	3	19	,	,	PUNCT
gra-920	3	20	and	and	CCONJ
gra-920	3	21	a	a	DET
gra-920	3	22	large	large	ADJ
gra-920	3	23	number	number	NOUN
gra-920	3	24	of	of	ADP
gra-920	3	25	reading	read	VERB
gra-920	3	26	segment	segment	NOUN
gra-920	3	27	data	datum	NOUN
gra-920	3	28	is	be	AUX
gra-920	3	29	obtained	obtain	VERB
gra-920	3	30	for	for	ADP
gra-920	3	31	researching	research	VERB
gra-920	3	32	the	the	DET
gra-920	3	33	expression	expression	NOUN
gra-920	3	34	degree	degree	NOUN
gra-920	3	35	of	of	ADP
gra-920	3	36	the	the	DET
gra-920	3	37	gene	gene	NOUN
gra-920	3	38	.	.	PUNCT
gra-920	4	1	with	with	ADP
gra-920	4	2	the	the	DET
gra-920	4	3	development	development	NOUN
gra-920	4	4	of	of	ADP
gra-920	4	5	sequencing	sequence	VERB
gra-920	4	6	technology	technology	NOUN
gra-920	4	7	,	,	PUNCT
gra-920	4	8	how	how	SCONJ
gra-920	4	9	to	to	PART
gra-920	4	10	analyze	analyze	VERB
gra-920	4	11	these	these	DET
gra-920	4	12	gene	gene	NOUN
gra-920	4	13	expression	expression	NOUN
gra-920	4	14	values	value	NOUN
gra-920	4	15	and	and	CCONJ
gra-920	4	16	process	process	VERB
gra-920	4	17	rna	rna	NOUN
gra-920	4	18	-	-	PUNCT
gra-920	4	19	seq	seq	NOUN
gra-920	4	20	data	data	NOUN
gra-920	4	21	is	be	AUX
gra-920	4	22	the	the	DET
gra-920	4	23	content	content	NOUN
gra-920	4	24	of	of	ADP
gra-920	4	25	this	this	DET
gra-920	4	26	paper	paper	NOUN
gra-920	4	27	.	.	PUNCT
gra-920	5	1	in	in	ADP
gra-920	5	2	biocomputing	biocomputing	NOUN
gra-920	5	3	,	,	PUNCT
gra-920	5	4	genes	gene	NOUN
gra-920	5	5	will	will	AUX
gra-920	5	6	have	have	VERB
gra-920	5	7	different	different	ADJ
gra-920	5	8	levels	level	NOUN
gra-920	5	9	of	of	ADP
gra-920	5	10	gene	gene	NOUN
gra-920	5	11	expression	expression	NOUN
gra-920	5	12	in	in	ADP
gra-920	5	13	different	different	ADJ
gra-920	5	14	situations	situation	NOUN
gra-920	5	15	[	[	X
gra-920	5	16	2	2	NUM
gra-920	5	17	]	]	PUNCT
gra-920	5	18	.	.	PUNCT
gra-920	6	1	the	the	DET
gra-920	6	2	common	common	ADJ
gra-920	6	3	regulation	regulation	NOUN
gra-920	6	4	of	of	ADP
gra-920	6	5	genes	gene	NOUN
gra-920	6	6	will	will	AUX
gra-920	6	7	affect	affect	VERB
gra-920	6	8	the	the	DET
gra-920	6	9	traits	trait	NOUN
gra-920	6	10	of	of	ADP
gra-920	6	11	organisms	organism	NOUN
gra-920	6	12	,	,	PUNCT
gra-920	6	13	so	so	SCONJ
gra-920	6	14	cluster	cluster	NOUN
gra-920	6	15	analysis	analysis	NOUN
gra-920	6	16	can	can	AUX
gra-920	6	17	cluster	cluster	VERB
gra-920	6	18	genes	gene	NOUN
gra-920	6	19	into	into	ADP
gra-920	6	20	different	different	ADJ
gra-920	6	21	classes	class	NOUN
gra-920	6	22	according	accord	VERB
gra-920	6	23	to	to	ADP
gra-920	6	24	different	different	ADJ
gra-920	6	25	expression	expression	NOUN
gra-920	6	26	patterns	pattern	NOUN
gra-920	6	27	.	.	PUNCT
gra-920	7	1	by	by	ADP
gra-920	7	2	clustering	cluster	VERB
gra-920	7	3	genes	gene	NOUN
gra-920	7	4	with	with	ADP
gra-920	7	5	similar	similar	ADJ
gra-920	7	6	functions	function	NOUN
gra-920	7	7	into	into	ADP
gra-920	7	8	the	the	DET
gra-920	7	9	same	same	ADJ
gra-920	7	10	class	class	NOUN
gra-920	7	11	,	,	PUNCT
gra-920	7	12	the	the	DET
gra-920	7	13	unknown	unknown	ADJ
gra-920	7	14	biological	biological	ADJ
gra-920	7	15	functions	function	NOUN
gra-920	7	16	of	of	ADP
gra-920	7	17	the	the	DET
gra-920	7	18	genes	gene	NOUN
gra-920	7	19	can	can	AUX
gra-920	7	20	be	be	AUX
gra-920	7	21	obtained	obtain	VERB
gra-920	7	22	.	.	PUNCT
gra-920	8	1	in	in	ADP
gra-920	8	2	this	this	DET
gra-920	8	3	paper	paper	NOUN
gra-920	8	4	,	,	PUNCT
gra-920	8	5	the	the	DET
gra-920	8	6	application	application	NOUN
gra-920	8	7	of	of	ADP
gra-920	8	8	some	some	DET
gra-920	8	9	clustering	clustering	ADJ
gra-920	8	10	algorithms	algorithm	NOUN
gra-920	8	11	in	in	ADP
gra-920	8	12	rna	rna	NOUN
gra-920	8	13	-	-	PUNCT
gra-920	8	14	seq	seq	NOUN
gra-920	8	15	is	be	AUX
gra-920	8	16	introduced	introduce	VERB
gra-920	8	17	.	.	PUNCT
gra-920	9	1	first	first	ADV
gra-920	9	2	,	,	PUNCT
gra-920	9	3	we	we	PRON
gra-920	9	4	need	need	VERB
gra-920	9	5	to	to	PART
gra-920	9	6	understand	understand	VERB
gra-920	9	7	the	the	DET
gra-920	9	8	application	application	NOUN
gra-920	9	9	of	of	ADP
gra-920	9	10	rna	rna	NOUN
gra-920	9	11	-	-	PUNCT
gra-920	9	12	seq	seq	NOUN
gra-920	9	13	technology	technology	NOUN
gra-920	9	14	.	.	PUNCT
gra-920	10	1	rna	rna	PROPN
gra-920	10	2	-	-	PUNCT
gra-920	10	3	seq	seq	NOUN
gra-920	10	4	,	,	PUNCT
gra-920	10	5	also	also	ADV
gra-920	10	6	known	know	VERB
gra-920	10	7	as	as	ADP
gra-920	10	8	full	full	ADJ
gra-920	10	9	transcription	transcription	NOUN
gra-920	10	10	shotgun	shotgun	NOUN
gra-920	10	11	sequencing	sequencing	NOUN
gra-920	10	12	,	,	PUNCT
gra-920	10	13	is	be	AUX
gra-920	10	14	the	the	DET
gra-920	10	15	next	next	ADJ
gra-920	10	16	generation	generation	NOUN
gra-920	10	17	sequencing	sequence	VERB
gra-920	10	18	technology	technology	NOUN
gra-920	10	19	to	to	PART
gra-920	10	20	sequence	sequence	VERB
gra-920	10	21	the	the	DET
gra-920	10	22	transcriptome	transcriptome	NOUN
gra-920	10	23	of	of	ADP
gra-920	10	24	biological	biological	ADJ
gra-920	10	25	samples	sample	NOUN
gra-920	10	26	[	[	X
gra-920	10	27	3	3	NUM
gra-920	10	28	]	]	PUNCT
gra-920	10	29	.	.	PUNCT
gra-920	11	1	this	this	DET
gra-920	11	2	technology	technology	NOUN
gra-920	11	3	extracts	extract	VERB
gra-920	11	4	all	all	DET
gra-920	11	5	the	the	DET
gra-920	11	6	transcriptional	transcriptional	ADJ
gra-920	11	7	rna	rna	NOUN
gra-920	11	8	from	from	ADP
gra-920	11	9	the	the	DET
gra-920	11	10	samples	sample	NOUN
gra-920	11	11	,	,	PUNCT
gra-920	11	12	and	and	CCONJ
gra-920	11	13	then	then	ADV
gra-920	11	14	through	through	ADP
gra-920	11	15	reverse	reverse	ADJ
gra-920	11	16	transcription	transcription	NOUN
gra-920	11	17	to	to	PART
gra-920	11	18	cdna	cdna	VERB
gra-920	11	19	for	for	ADP
gra-920	11	20	sequencing	sequence	VERB
gra-920	11	21	,	,	PUNCT
gra-920	11	22	so	so	SCONJ
gra-920	11	23	as	as	SCONJ
gra-920	11	24	to	to	PART
gra-920	11	25	estimate	estimate	VERB
gra-920	11	26	the	the	DET
gra-920	11	27	expression	expression	NOUN
gra-920	11	28	form	form	NOUN
gra-920	11	29	and	and	CCONJ
gra-920	11	30	level	level	NOUN
gra-920	11	31	of	of	ADP
gra-920	11	32	rna	rna	NOUN
gra-920	11	33	,	,	PUNCT
gra-920	11	34	and	and	CCONJ
gra-920	11	35	understand	understand	VERB
gra-920	11	36	and	and	CCONJ
gra-920	11	37	analyze	analyze	VERB
gra-920	11	38	the	the	DET
gra-920	11	39	gene	gene	NOUN
gra-920	11	40	expression	expression	NOUN
gra-920	11	41	.	.	PUNCT
gra-920	12	1	the	the	DET
gra-920	12	2	resulting	result	VERB
gra-920	12	3	data	datum	NOUN
gra-920	12	4	is	be	AUX
gra-920	12	5	getting	get	VERB
gra-920	12	6	larger	large	ADJ
gra-920	12	7	and	and	CCONJ
gra-920	12	8	more	more	ADV
gra-920	12	9	diverse	diverse	ADJ
gra-920	12	10	,	,	PUNCT
gra-920	12	11	so	so	ADV
gra-920	12	12	on	on	ADP
gra-920	12	13	this	this	DET
gra-920	12	14	basis	basis	NOUN
gra-920	12	15	,	,	PUNCT
gra-920	12	16	the	the	DET
gra-920	12	17	processing	processing	NOUN
gra-920	12	18	of	of	ADP
gra-920	12	19	data	datum	NOUN
gra-920	12	20	through	through	ADP
gra-920	12	21	data	datum	NOUN
gra-920	12	22	mining	mining	NOUN
gra-920	12	23	technology	technology	NOUN
gra-920	12	24	also	also	ADV
gra-920	12	25	came	come	VERB
gra-920	12	26	into	into	ADP
gra-920	12	27	being	being	NOUN
gra-920	12	28	.	.	PUNCT
gra-920	13	1	clustering	cluster	VERB
gra-920	13	2	[	[	X
gra-920	13	3	4	4	NUM
gra-920	13	4	]	]	PUNCT
gra-920	13	5	is	be	AUX
gra-920	13	6	to	to	PART
gra-920	13	7	divide	divide	VERB
gra-920	13	8	the	the	DET
gra-920	13	9	data	datum	NOUN
gra-920	13	10	set	set	VERB
gra-920	13	11	in	in	ADP
gra-920	13	12	the	the	DET
gra-920	13	13	sample	sample	NOUN
gra-920	13	14	into	into	ADP
gra-920	13	15	different	different	ADJ
gra-920	13	16	classes	class	NOUN
gra-920	13	17	according	accord	VERB
gra-920	13	18	to	to	ADP
gra-920	13	19	the	the	DET
gra-920	13	20	internal	internal	ADJ
gra-920	13	21	connection	connection	NOUN
gra-920	13	22	,	,	PUNCT
gra-920	13	23	and	and	CCONJ
gra-920	13	24	make	make	VERB
gra-920	13	25	the	the	DET
gra-920	13	26	same	same	ADJ
gra-920	13	27	class	class	NOUN
gra-920	13	28	have	have	VERB
gra-920	13	29	great	great	ADJ
gra-920	13	30	similarity	similarity	NOUN
gra-920	13	31	.	.	PUNCT
gra-920	14	1	there	there	PRON
gra-920	14	2	are	be	VERB
gra-920	14	3	four	four	NUM
gra-920	14	4	types	type	NOUN
gra-920	14	5	of	of	ADP
gra-920	14	6	clustering	clustering	ADJ
gra-920	14	7	algorithm	algorithm	NOUN
gra-920	14	8	:	:	PUNCT
gra-920	14	9	distributed	distribute	VERB
gra-920	14	10	,	,	PUNCT
gra-920	14	11	structured	structure	VERB
gra-920	14	12	,	,	PUNCT
gra-920	14	13	density	density	NOUN
gra-920	14	14	and	and	CCONJ
gra-920	14	15	graph	graph	NOUN
gra-920	14	16	structured	structure	VERB
gra-920	14	17	.	.	PUNCT
gra-920	15	1	distributed	distribute	VERB
gra-920	15	2	clustering	cluster	VERB
gra-920	15	3	[	[	X
gra-920	15	4	5	5	NUM
gra-920	15	5	]	]	PUNCT
gra-920	15	6	is	be	AUX
gra-920	15	7	a	a	DET
gra-920	15	8	kind	kind	NOUN
gra-920	15	9	of	of	ADP
gra-920	15	10	clustering	clustering	NOUN
gra-920	15	11	that	that	PRON
gra-920	15	12	can	can	AUX
gra-920	15	13	be	be	AUX
gra-920	15	14	generated	generate	VERB
gra-920	15	15	at	at	ADP
gra-920	15	16	one	one	NUM
gra-920	15	17	time	time	NOUN
gra-920	15	18	,	,	PUNCT
gra-920	15	19	and	and	CCONJ
gra-920	15	20	the	the	DET
gra-920	15	21	representative	representative	ADJ
gra-920	15	22	algorithm	algorithm	NOUN
gra-920	15	23	is	be	AUX
gra-920	15	24	k	k	NOUN
gra-920	15	25	-	-	PUNCT
gra-920	15	26	means	means	NOUN
gra-920	15	27	clustering	clustering	NOUN
gra-920	15	28	.	.	PUNCT
gra-920	16	1	structural	structural	ADJ
gra-920	16	2	clustering	clustering	NOUN
gra-920	16	3	uses	use	VERB
gra-920	16	4	aggregators	aggregator	NOUN
gra-920	16	5	that	that	PRON
gra-920	16	6	have	have	AUX
gra-920	16	7	been	be	AUX
gra-920	16	8	successfully	successfully	ADV
gra-920	16	9	classied	classied	PROPN
gra-920	16	10	,	,	PUNCT
gra-920	16	11	which	which	PRON
gra-920	16	12	can	can	AUX
gra-920	16	13	be	be	AUX
gra-920	16	14	calculated	calculate	VERB
gra-920	16	15	from	from	ADP
gra-920	16	16	top	top	NOUN
gra-920	16	17	to	to	ADP
gra-920	16	18	bottom	bottom	NOUN
gra-920	16	19	or	or	CCONJ
gra-920	16	20	from	from	ADP
gra-920	16	21	bottom	bottom	NOUN
gra-920	16	22	to	to	ADP
gra-920	16	23	top	top	NOUN
gra-920	16	24	,	,	PUNCT
gra-920	16	25	which	which	PRON
gra-920	16	26	represents	represent	VERB
gra-920	16	27	hierarchical	hierarchical	ADJ
gra-920	16	28	clustering	clustering	NOUN
gra-920	16	29	.	.	PUNCT
gra-920	17	1	the	the	DET
gra-920	17	2	density	density	NOUN
gra-920	17	3	clustering	cluster	VERB
gra-920	17	4	algorithm	algorithm	NOUN
gra-920	17	5	is	be	AUX
gra-920	17	6	used	use	VERB
gra-920	17	7	for	for	ADP
gra-920	17	8	categories	category	NOUN
gra-920	17	9	of	of	ADP
gra-920	17	10	arbitrary	arbitrary	ADJ
gra-920	17	11	shape	shape	NOUN
gra-920	17	12	features	feature	NOUN
gra-920	17	13	,	,	PUNCT
gra-920	17	14	and	and	CCONJ
gra-920	17	15	these	these	DET
gra-920	17	16	categories	category	NOUN
gra-920	17	17	are	be	AUX
gra-920	17	18	considered	consider	VERB
gra-920	17	19	as	as	SCONJ
gra-920	17	20	areas	area	NOUN
gra-920	17	21	in	in	ADP
gra-920	17	22	the	the	DET
gra-920	17	23	data	datum	NOUN
gra-920	17	24	set	set	VERB
gra-920	17	25	that	that	PRON
gra-920	17	26	are	be	AUX
gra-920	17	27	larger	large	ADJ
gra-920	17	28	than	than	ADP
gra-920	17	29	a	a	DET
gra-920	17	30	certain	certain	ADJ
gra-920	17	31	threshold	threshold	NOUN
gra-920	17	32	.	.	PUNCT
gra-920	18	1	the	the	DET
gra-920	18	2	graph	graph	NOUN
gra-920	18	3	structured	structure	VERB
gra-920	18	4	clustering	clustering	ADJ
gra-920	18	5	algorithm	algorithm	NOUN
gra-920	18	6	only	only	ADV
gra-920	18	7	returns	return	VERB
gra-920	18	8	one	one	NUM
gra-920	18	9	solution	solution	NOUN
gra-920	18	10	,	,	PUNCT
gra-920	18	11	which	which	PRON
gra-920	18	12	can	can	AUX
gra-920	18	13	reduce	reduce	VERB
gra-920	18	14	the	the	DET
gra-920	18	15	running	run	VERB
gra-920	18	16	cost	cost	NOUN
gra-920	18	17	,	,	PUNCT
gra-920	18	18	and	and	CCONJ
gra-920	18	19	the	the	DET
gra-920	18	20	representative	representative	ADJ
gra-920	18	21	algorithm	algorithm	NOUN
gra-920	18	22	is	be	AUX
gra-920	18	23	louvain	louvain	NOUN
gra-920	18	24	algorithm	algorithm	NOUN
gra-920	18	25	.	.	PUNCT
gra-920	19	1	2	2	X
gra-920	19	2	.	.	X
gra-920	19	3	algorithm	algorithm	PROPN
gra-920	19	4	research	research	NOUN
gra-920	19	5	method	method	NOUN
gra-920	19	6	2.1	2.1	NUM
gra-920	19	7	k	k	ADJ
gra-920	19	8	-	-	ADJ
gra-920	19	9	mean	mean	ADJ
gra-920	19	10	algorithm	algorithm	NOUN
gra-920	19	11	k	k	ADJ
gra-920	19	12	-	-	ADJ
gra-920	19	13	mean	mean	ADJ
gra-920	19	14	algorithm	algorithm	NOUN
gra-920	19	15	is	be	AUX
gra-920	19	16	a	a	DET
gra-920	19	17	relatively	relatively	ADV
gra-920	19	18	simple	simple	ADJ
gra-920	19	19	algorithm	algorithm	NOUN
gra-920	19	20	in	in	ADP
gra-920	19	21	clustering	clustering	NOUN
gra-920	19	22	,	,	PUNCT
gra-920	19	23	which	which	PRON
gra-920	19	24	can	can	AUX
gra-920	19	25	be	be	AUX
gra-920	19	26	applied	apply	VERB
gra-920	19	27	to	to	ADP
gra-920	19	28	many	many	ADJ
gra-920	19	29	elds	eld	NOUN
gra-920	19	30	.	.	PUNCT
gra-920	19	31	clustering	cluster	VERB
gra-920	19	32	cells	cell	NOUN
gra-920	19	33	with	with	ADP
gra-920	19	34	the	the	DET
gra-920	19	35	k	k	ADJ
gra-920	19	36	-	-	ADJ
gra-920	19	37	mean	mean	ADJ
gra-920	19	38	algorithm	algorithm	NOUN
gra-920	19	39	is	be	AUX
gra-920	19	40	also	also	ADV
gra-920	19	41	a	a	DET
gra-920	19	42	successful	successful	ADJ
gra-920	19	43	method	method	NOUN
gra-920	19	44	.	.	PUNCT
gra-920	20	1	given	give	VERB
gra-920	20	2	a	a	DET
gra-920	20	3	set	set	NOUN
gra-920	20	4	of	of	ADP
gra-920	20	5	data	datum	NOUN
gra-920	20	6	(	(	PUNCT
gra-920	20	7	x	x	X
gra-920	20	8	,	,	PUNCT
gra-920	20	9	x	x	X
gra-920	20	10	,	,	PUNCT
gra-920	20	11	…	…	PUNCT
gra-920	20	12	x	x	SYM
gra-920	20	13	)	)	PUNCT
gra-920	20	14	,	,	PUNCT
gra-920	20	15	divide	divide	VERB
gra-920	20	16	the	the	DET
gra-920	20	17	n	n	NUM
gra-920	20	18	data	datum	NOUN
gra-920	20	19	into	into	ADP
gra-920	20	20	k	k	PROPN
gra-920	20	21	sets	set	VERB
gra-920	20	22	so	so	SCONJ
gra-920	20	23	1	1	NUM
gra-920	20	24	2	2	NUM
gra-920	20	25	n	n	NOUN
gra-920	20	26	that	that	PRON
gra-920	20	27	the	the	DET
gra-920	20	28	variance	variance	NOUN
gra-920	20	29	within	within	ADP
gra-920	20	30	each	each	DET
gra-920	20	31	class	class	NOUN
gra-920	20	32	is	be	AUX
gra-920	20	33	minimized	minimize	VERB
gra-920	20	34	.	.	PUNCT
gra-920	21	1	the	the	DET
gra-920	21	2	k	k	PROPN
gra-920	21	3	initial	initial	ADJ
gra-920	21	4	points	point	NOUN
gra-920	21	5	are	be	AUX
gra-920	21	6	randomly	randomly	ADV
gra-920	21	7	selected	select	VERB
gra-920	21	8	from	from	ADP
gra-920	21	9	the	the	DET
gra-920	21	10	training	training	NOUN
gra-920	21	11	data	datum	NOUN
gra-920	21	12	as	as	ADP
gra-920	21	13	the	the	DET
gra-920	21	14	initial	initial	ADJ
gra-920	21	15	points	point	NOUN
gra-920	21	16	of	of	ADP
gra-920	21	17	the	the	DET
gra-920	21	18	class	class	NOUN
gra-920	21	19	,	,	PUNCT
gra-920	21	20	the	the	DET
gra-920	21	21	euclidean	euclidean	ADJ
gra-920	21	22	distance	distance	NOUN
gra-920	21	23	from	from	ADP
gra-920	21	24	each	each	DET
gra-920	21	25	point	point	NOUN
gra-920	21	26	to	to	ADP
gra-920	21	27	the	the	DET
gra-920	21	28	center	center	NOUN
gra-920	21	29	point	point	NOUN
gra-920	21	30	is	be	AUX
gra-920	21	31	calculated	calculate	VERB
gra-920	21	32	,	,	PUNCT
gra-920	21	33	and	and	CCONJ
gra-920	21	34	the	the	DET
gra-920	21	35	mean	mean	ADJ
gra-920	21	36	value	value	NOUN
gra-920	21	37	of	of	ADP
gra-920	21	38	the	the	DET
gra-920	21	39	data	data	NOUN
gra-920	21	40	points	point	NOUN
gra-920	21	41	in	in	ADP
gra-920	21	42	each	each	DET
gra-920	21	43	class	class	NOUN
gra-920	21	44	is	be	AUX
gra-920	21	45	calculated	calculate	VERB
gra-920	21	46	as	as	ADP
gra-920	21	47	the	the	DET
gra-920	21	48	new	new	ADJ
gra-920	21	49	center	center	NOUN
gra-920	21	50	point	point	NOUN
gra-920	21	51	.	.	PUNCT
gra-920	22	1	if	if	SCONJ
gra-920	22	2	there	there	PRON
gra-920	22	3	is	be	VERB
gra-920	22	4	no	no	DET
gra-920	22	5	change	change	NOUN
gra-920	22	6	relative	relative	ADJ
gra-920	22	7	to	to	ADP
gra-920	22	8	the	the	DET
gra-920	22	9	original	original	ADJ
gra-920	22	10	center	center	NOUN
gra-920	22	11	point	point	NOUN
gra-920	22	12	or	or	CCONJ
gra-920	22	13	the	the	DET
gra-920	22	14	change	change	NOUN
gra-920	22	15	value	value	NOUN
gra-920	22	16	is	be	AUX
gra-920	22	17	less	less	ADJ
gra-920	22	18	than	than	ADP
gra-920	22	19	the	the	DET
gra-920	22	20	threshold	threshold	NOUN
gra-920	22	21	,	,	PUNCT
gra-920	22	22	then	then	ADV
gra-920	22	23	the	the	DET
gra-920	22	24	algorithm	algorithm	NOUN
gra-920	22	25	ends	end	VERB
gra-920	22	26	,	,	PUNCT
gra-920	22	27	otherwise	otherwise	ADV
gra-920	22	28	the	the	DET
gra-920	22	29	algorithm	algorithm	NOUN
gra-920	22	30	is	be	AUX
gra-920	22	31	repeated	repeat	VERB
gra-920	22	32	.	.	PUNCT
gra-920	23	1	the	the	DET
gra-920	23	2	key	key	NOUN
gra-920	23	3	of	of	ADP
gra-920	23	4	k	k	ADJ
gra-920	23	5	-	-	PUNCT
gra-920	23	6	mean	mean	ADJ
gra-920	23	7	algorithm	algorithm	NOUN
gra-920	23	8	is	be	AUX
gra-920	23	9	the	the	DET
gra-920	23	10	choice	choice	NOUN
gra-920	23	11	of	of	ADP
gra-920	23	12	k	k	PROPN
gra-920	23	13	value	value	NOUN
gra-920	23	14	and	and	CCONJ
gra-920	23	15	initial	initial	ADJ
gra-920	23	16	point	point	NOUN
gra-920	23	17	.	.	PUNCT
gra-920	24	1	the	the	DET
gra-920	24	2	value	value	NOUN
gra-920	24	3	range	range	NOUN
gra-920	24	4	of	of	ADP
gra-920	24	5	k	k	PROPN
gra-920	24	6	value	value	NOUN
gra-920	24	7	is	be	AUX
gra-920	24	8	[	[	X
gra-920	24	9	-1	-1	ADJ
gra-920	24	10	,	,	PUNCT
gra-920	24	11	1	1	NUM
gra-920	24	12	]	]	PUNCT
gra-920	24	13	.	.	PUNCT
gra-920	25	1	the	the	PRON
gra-920	25	2	larger	large	ADJ
gra-920	25	3	the	the	DET
gra-920	25	4	value	value	NOUN
gra-920	25	5	of	of	ADP
gra-920	25	6	k	k	PROPN
gra-920	25	7	,	,	PUNCT
gra-920	25	8	the	the	PRON
gra-920	25	9	better	well	ADJ
gra-920	25	10	the	the	DET
gra-920	25	11	result	result	NOUN
gra-920	25	12	.	.	PUNCT
gra-920	26	1	2.2	2.2	NUM
gra-920	26	2	hierarchical	hierarchical	ADJ
gra-920	26	3	clustering	clustering	ADJ
gra-920	26	4	algorithm	algorithm	NOUN
gra-920	26	5	method	method	NOUN
gra-920	26	6	hierarchical	hierarchical	ADJ
gra-920	26	7	clustering	clustering	ADJ
gra-920	26	8	algorithm	algorithm	NOUN
gra-920	26	9	is	be	AUX
gra-920	26	10	a	a	DET
gra-920	26	11	kind	kind	NOUN
gra-920	26	12	of	of	ADP
gra-920	26	13	structured	structured	ADJ
gra-920	26	14	clustering	clustering	NOUN
gra-920	26	15	method	method	NOUN
gra-920	26	16	,	,	PUNCT
gra-920	26	17	which	which	PRON
gra-920	26	18	constructs	construct	VERB
gra-920	26	19	hierarchical	hierarchical	ADJ
gra-920	26	20	clustering	clustering	NOUN
gra-920	26	21	tree	tree	NOUN
gra-920	26	22	by	by	ADP
gra-920	26	23	calculating	calculate	VERB
gra-920	26	24	the	the	DET
gra-920	26	25	similarity	similarity	NOUN
gra-920	26	26	between	between	ADP
gra-920	26	27	different	different	ADJ
gra-920	26	28	categories	category	NOUN
gra-920	26	29	of	of	ADP
gra-920	26	30	data	datum	NOUN
gra-920	26	31	points	point	NOUN
gra-920	26	32	.	.	PUNCT
gra-920	27	1	the	the	DET
gra-920	27	2	creation	creation	NOUN
gra-920	27	3	of	of	ADP
gra-920	27	4	hierarchical	hierarchical	ADJ
gra-920	27	5	clustering	clustering	NOUN
gra-920	27	6	tree	tree	NOUN
gra-920	27	7	is	be	AUX
gra-920	27	8	divided	divide	VERB
gra-920	27	9	into	into	ADP
gra-920	27	10	two	two	NUM
gra-920	27	11	ways	way	NOUN
gra-920	27	12	:	:	PUNCT
gra-920	27	13	top	top	ADJ
gra-920	27	14	-	-	PUNCT
gra-920	27	15	down	down	ADP
gra-920	27	16	splitting	splitting	NOUN
gra-920	27	17	and	and	CCONJ
gra-920	27	18	bottom	bottom	NOUN
gra-920	27	19	-	-	PUNCT
gra-920	27	20	up	up	ADP
gra-920	27	21	merging	merge	VERB
gra-920	27	22	.	.	PUNCT
gra-920	28	1	hierarchical	hierarchical	ADJ
gra-920	28	2	clustering	clustering	NOUN
gra-920	28	3	can	can	AUX
gra-920	28	4	be	be	AUX
gra-920	28	5	divided	divide	VERB
gra-920	28	6	into	into	ADP
gra-920	28	7	two	two	NUM
gra-920	28	8	types	type	NOUN
gra-920	28	9	:	:	PUNCT
gra-920	28	10	splitting	splitting	NOUN
gra-920	28	11	and	and	CCONJ
gra-920	28	12	cohesion	cohesion	NOUN
gra-920	28	13	[	[	X
gra-920	28	14	6	6	NUM
gra-920	28	15	]	]	PUNCT
gra-920	28	16	.	.	PUNCT
gra-920	29	1	splitting	splitting	NOUN
gra-920	29	2	is	be	AUX
gra-920	29	3	a	a	DET
gra-920	29	4	top	top	ADJ
gra-920	29	5	-	-	PUNCT
gra-920	29	6	down	down	ADP
gra-920	29	7	idea	idea	NOUN
gra-920	29	8	.	.	PUNCT
gra-920	30	1	at	at	ADP
gra-920	30	2	the	the	DET
gra-920	30	3	beginning	beginning	NOUN
gra-920	30	4	,	,	PUNCT
gra-920	30	5	all	all	DET
gra-920	30	6	data	datum	NOUN
gra-920	30	7	are	be	AUX
gra-920	30	8	regarded	regard	VERB
gra-920	30	9	as	as	ADP
gra-920	30	10	a	a	DET
gra-920	30	11	class	class	NOUN
gra-920	30	12	until	until	SCONJ
gra-920	30	13	there	there	PRON
gra-920	30	14	is	be	VERB
gra-920	30	15	only	only	ADV
gra-920	30	16	one	one	NUM
gra-920	30	17	sample	sample	NOUN
gra-920	30	18	in	in	ADP
gra-920	30	19	the	the	DET
gra-920	30	20	class	class	NOUN
gra-920	30	21	.	.	PUNCT
gra-920	31	1	cohesion	cohesion	NOUN
gra-920	31	2	is	be	AUX
gra-920	31	3	a	a	DET
gra-920	31	4	bottom	bottom	ADJ
gra-920	31	5	-	-	PUNCT
gra-920	31	6	up	up	ADP
gra-920	31	7	idea	idea	NOUN
gra-920	31	8	[	[	X
gra-920	31	9	7	7	NUM
gra-920	31	10	]	]	PUNCT
gra-920	31	11	.	.	PUNCT
gra-920	32	1	at	at	ADP
gra-920	32	2	the	the	DET
gra-920	32	3	beginning	beginning	NOUN
gra-920	32	4	,	,	PUNCT
gra-920	32	5	each	each	DET
gra-920	32	6	sample	sample	NOUN
gra-920	32	7	is	be	AUX
gra-920	32	8	regarded	regard	VERB
gra-920	32	9	as	as	ADP
gra-920	32	10	a	a	DET
gra-920	32	11	different	different	ADJ
gra-920	32	12	class	class	NOUN
gra-920	32	13	,	,	PUNCT
gra-920	32	14	and	and	CCONJ
gra-920	32	15	the	the	DET
gra-920	32	16	latest	late	ADJ
gra-920	32	17	pair	pair	NOUN
gra-920	32	18	of	of	ADP
gra-920	32	19	classes	class	NOUN
gra-920	32	20	are	be	AUX
gra-920	32	21	combined	combine	VERB
gra-920	32	22	repeatedly	repeatedly	ADV
gra-920	32	23	until	until	SCONJ
gra-920	32	24	all	all	DET
gra-920	32	25	the	the	DET
gra-920	32	26	data	datum	NOUN
gra-920	32	27	belong	belong	VERB
gra-920	32	28	to	to	ADP
gra-920	32	29	the	the	DET
gra-920	32	30	same	same	ADJ
gra-920	32	31	class	class	NOUN
gra-920	32	32	.	.	PUNCT
gra-920	33	1	there	there	PRON
gra-920	33	2	are	be	VERB
gra-920	33	3	three	three	NUM
gra-920	33	4	methods	method	NOUN
gra-920	33	5	for	for	ADP
gra-920	33	6	determining	determine	VERB
gra-920	33	7	the	the	DET
gra-920	33	8	distance	distance	NOUN
gra-920	33	9	by	by	ADP
gra-920	33	10	the	the	DET
gra-920	33	11	agglomeration	agglomeration	NOUN
gra-920	33	12	hierarchical	hierarchical	ADJ
gra-920	33	13	clustering	clustering	ADJ
gra-920	33	14	algorithm	algorithm	NOUN
gra-920	33	15	:	:	PUNCT
gra-920	33	16	single	single	ADJ
gra-920	33	17	linkage	linkage	NOUN
gra-920	33	18	,	,	PUNCT
gra-920	33	19	complete	complete	ADJ
gra-920	33	20	linkage	linkage	NOUN
gra-920	33	21	,	,	PUNCT
gra-920	33	22	average	average	ADJ
gra-920	33	23	linkage	linkage	NOUN
gra-920	33	24	.	.	PUNCT
gra-920	34	1	the	the	DET
gra-920	34	2	distance	distance	NOUN
gra-920	34	3	determined	determine	VERB
gra-920	34	4	by	by	ADP
gra-920	34	5	the	the	DET
gra-920	34	6	rst	rst	PROPN
gra-920	34	7	method	method	NOUN
gra-920	34	8	is	be	AUX
gra-920	34	9	the	the	DET
gra-920	34	10	distance	distance	NOUN
gra-920	34	11	when	when	SCONJ
gra-920	34	12	the	the	DET
gra-920	34	13	sample	sample	NOUN
gra-920	34	14	points	point	VERB
gra-920	34	15	in	in	ADP
gra-920	34	16	the	the	DET
gra-920	34	17	two	two	NUM
gra-920	34	18	classes	class	NOUN
gra-920	34	19	are	be	AUX
gra-920	34	20	closest	close	ADJ
gra-920	34	21	.	.	PUNCT
gra-920	35	1	the	the	DET
gra-920	35	2	distance	distance	NOUN
gra-920	35	3	determined	determine	VERB
gra-920	35	4	by	by	ADP
gra-920	35	5	the	the	DET
gra-920	35	6	second	second	ADJ
gra-920	35	7	method	method	NOUN
gra-920	35	8	is	be	AUX
gra-920	35	9	the	the	DET
gra-920	35	10	distance	distance	NOUN
gra-920	35	11	when	when	SCONJ
gra-920	35	12	the	the	DET
gra-920	35	13	sample	sample	NOUN
gra-920	35	14	points	point	VERB
gra-920	35	15	in	in	ADP
gra-920	35	16	the	the	DET
gra-920	35	17	two	two	NUM
gra-920	35	18	classes	class	NOUN
gra-920	35	19	are	be	AUX
gra-920	35	20	farthest	farth	ADJ
gra-920	35	21	,	,	PUNCT
gra-920	35	22	which	which	PRON
gra-920	35	23	may	may	AUX
gra-920	35	24	make	make	VERB
gra-920	35	25	the	the	DET
gra-920	35	26	two	two	NUM
gra-920	35	27	similar	similar	ADJ
gra-920	35	28	classes	class	NOUN
gra-920	35	29	unable	unable	ADJ
gra-920	35	30	to	to	PART
gra-920	35	31	be	be	AUX
gra-920	35	32	combined	combine	VERB
gra-920	35	33	because	because	SCONJ
gra-920	35	34	the	the	DET
gra-920	35	35	extreme	extreme	ADJ
gra-920	35	36	values	value	NOUN
gra-920	35	37	are	be	AUX
gra-920	35	38	too	too	ADV
gra-920	35	39	far	far	ADV
gra-920	35	40	.	.	PUNCT
gra-920	36	1	the	the	DET
gra-920	36	2	last	last	ADJ
gra-920	36	3	method	method	NOUN
gra-920	36	4	is	be	AUX
gra-920	36	5	to	to	PART
gra-920	36	6	calculate	calculate	VERB
gra-920	36	7	the	the	DET
gra-920	36	8	mean	mean	ADJ
gra-920	36	9	value	value	NOUN
gra-920	36	10	of	of	ADP
gra-920	36	11	all	all	DET
gra-920	36	12	distances	distance	NOUN
gra-920	36	13	between	between	ADP
gra-920	36	14	samples	sample	NOUN
gra-920	36	15	.	.	PUNCT
gra-920	37	1	although	although	SCONJ
gra-920	37	2	this	this	DET
gra-920	37	3	method	method	NOUN
gra-920	37	4	has	have	VERB
gra-920	37	5	a	a	DET
gra-920	37	6	large	large	ADJ
gra-920	37	7	amount	amount	NOUN
gra-920	37	8	of	of	ADP
gra-920	37	9	calculation	calculation	NOUN
gra-920	37	10	,	,	PUNCT
gra-920	37	11	the	the	DET
gra-920	37	12	results	result	NOUN
gra-920	37	13	are	be	AUX
gra-920	37	14	reasonable	reasonable	ADJ
gra-920	37	15	.	.	PUNCT
gra-920	38	1	2.3	2.3	NUM
gra-920	38	2	graph	graph	NOUN
gra-920	38	3	structure	structure	NOUN
gra-920	38	4	clustering	cluster	VERB
gra-920	38	5	algorithm	algorithm	NOUN
gra-920	38	6	lougain	lougain	VERB
gra-920	38	7	[	[	X
gra-920	38	8	8	8	NUM
gra-920	38	9	]	]	PUNCT
gra-920	38	10	algorithm	algorithm	NOUN
gra-920	38	11	is	be	AUX
gra-920	38	12	a	a	DET
gra-920	38	13	heuristic	heuristic	ADJ
gra-920	38	14	clustering	cluster	VERB
gra-920	38	15	algorithm	algorithm	NOUN
gra-920	38	16	based	base	VERB
gra-920	38	17	on	on	ADP
gra-920	38	18	optimization	optimization	NOUN
gra-920	38	19	.	.	PUNCT
gra-920	39	1	the	the	DET
gra-920	39	2	implementation	implementation	NOUN
gra-920	39	3	of	of	ADP
gra-920	39	4	this	this	DET
gra-920	39	5	algorithm	algorithm	NOUN
gra-920	39	6	mainly	mainly	ADV
gra-920	39	7	consists	consist	VERB
gra-920	39	8	of	of	ADP
gra-920	39	9	two	two	NUM
gra-920	39	10	steps	step	NOUN
gra-920	39	11	.	.	PUNCT
gra-920	40	1	firstly	firstly	ADV
gra-920	40	2	,	,	PUNCT
gra-920	40	3	the	the	DET
gra-920	40	4	nodes	node	NOUN
gra-920	40	5	in	in	ADP
gra-920	40	6	the	the	DET
gra-920	40	7	network	network	NOUN
gra-920	40	8	are	be	AUX
gra-920	40	9	traversed	traverse	VERB
gra-920	40	10	continuously	continuously	ADV
gra-920	40	11	,	,	PUNCT
gra-920	40	12	and	and	CCONJ
gra-920	40	13	all	all	DET
gra-920	40	14	the	the	DET
gra-920	40	15	neighbors	neighbor	NOUN
gra-920	40	16	are	be	AUX
gra-920	40	17	traversed	traverse	VERB
gra-920	40	18	for	for	ADP
gra-920	40	19	each	each	DET
gra-920	40	20	node	node	NOUN
gra-920	40	21	.	.	PUNCT
gra-920	41	1	the	the	DET
gra-920	41	2	income	income	NOUN
gra-920	41	3	generated	generate	VERB
gra-920	41	4	by	by	ADP
gra-920	41	5	adding	add	VERB
gra-920	41	6	neighbors	neighbor	NOUN
gra-920	41	7	to	to	ADP
gra-920	41	8	the	the	DET
gra-920	41	9	node	node	NOUN
gra-920	41	10	is	be	AUX
gra-920	41	11	calculated	calculate	VERB
gra-920	41	12	,	,	PUNCT
gra-920	41	13	and	and	CCONJ
gra-920	41	14	the	the	DET
gra-920	41	15	neighbor	neighbor	NOUN
gra-920	41	16	who	who	PRON
gra-920	41	17	gets	get	VERB
gra-920	41	18	the	the	DET
gra-920	41	19	maximum	maximum	ADJ
gra-920	41	20	income	income	NOUN
gra-920	41	21	is	be	AUX
gra-920	41	22	selected	select	VERB
gra-920	41	23	to	to	PART
gra-920	41	24	join	join	VERB
gra-920	41	25	the	the	DET
gra-920	41	26	group	group	NOUN
gra-920	41	27	.	.	PUNCT
gra-920	42	1	if	if	SCONJ
gra-920	42	2	there	there	PRON
gra-920	42	3	is	be	VERB
gra-920	42	4	no	no	DET
gra-920	42	5	income	income	NOUN
gra-920	42	6	or	or	CCONJ
gra-920	42	7	the	the	DET
gra-920	42	8	income	income	NOUN
gra-920	42	9	is	be	AUX
gra-920	42	10	negative	negative	ADJ
gra-920	42	11	,	,	PUNCT
gra-920	42	12	the	the	DET
gra-920	42	13	node	node	NOUN
gra-920	42	14	remains	remain	VERB
gra-920	42	15	in	in	ADP
gra-920	42	16	the	the	DET
gra-920	42	17	group	group	NOUN
gra-920	42	18	.	.	PUNCT
gra-920	43	1	repeat	repeat	VERB
gra-920	43	2	the	the	DET
gra-920	43	3	process	process	NOUN
gra-920	43	4	until	until	SCONJ
gra-920	43	5	all	all	DET
gra-920	43	6	the	the	DET
gra-920	43	7	nodes	node	NOUN
gra-920	43	8	no	no	ADV
gra-920	43	9	longer	long	ADV
gra-920	43	10	change	change	VERB
gra-920	43	11	.	.	PUNCT
gra-920	44	1	secondly	secondly	ADV
gra-920	44	2	,	,	PUNCT
gra-920	44	3	the	the	DET
gra-920	44	4	group	group	NOUN
gra-920	44	5	formed	form	VERB
gra-920	44	6	in	in	ADP
gra-920	44	7	the	the	DET
gra-920	44	8	rst	rst	PROPN
gra-920	44	9	stage	stage	NOUN
gra-920	44	10	is	be	AUX
gra-920	44	11	folded	fold	VERB
gra-920	44	12	,	,	PUNCT
gra-920	44	13	each	each	DET
gra-920	44	14	combination	combination	NOUN
gra-920	44	15	is	be	AUX
gra-920	44	16	combined	combine	VERB
gra-920	44	17	and	and	CCONJ
gra-920	44	18	the	the	DET
gra-920	44	19	super	super	ADJ
gra-920	44	20	node	node	NOUN
gra-920	44	21	is	be	AUX
gra-920	44	22	taken	take	VERB
gra-920	44	23	as	as	ADP
gra-920	44	24	a	a	DET
gra-920	44	25	super	super	ADJ
gra-920	44	26	node	node	NOUN
gra-920	44	27	to	to	PART
gra-920	44	28	reconstruct	reconstruct	VERB
gra-920	44	29	the	the	DET
gra-920	44	30	network	network	NOUN
gra-920	44	31	,	,	PUNCT
gra-920	44	32	and	and	CCONJ
gra-920	44	33	the	the	DET
gra-920	44	34	weight	weight	NOUN
gra-920	44	35	value	value	NOUN
gra-920	44	36	among	among	ADP
gra-920	44	37	the	the	DET
gra-920	44	38	new	new	ADJ
gra-920	44	39	nodes	node	NOUN
gra-920	44	40	is	be	AUX
gra-920	44	41	calculated	calculate	VERB
gra-920	44	42	.	.	PUNCT
gra-920	45	1	the	the	DET
gra-920	45	2	two	two	NUM
gra-920	45	3	steps	step	NOUN
gra-920	45	4	are	be	AUX
gra-920	45	5	iterated	iterate	VERB
gra-920	45	6	analysis	analysis	NOUN
gra-920	45	7	of	of	ADP
gra-920	45	8	differential	differential	ADJ
gra-920	45	9	gene	gene	NOUN
gra-920	45	10	expression	expression	NOUN
gra-920	45	11	levels	level	NOUN
gra-920	45	12	based	base	VERB
gra-920	45	13	on	on	ADP
gra-920	45	14	rna	rna	NOUN
gra-920	45	15	-	-	PUNCT
gra-920	45	16	seq	seq	NOUN
gra-920	45	17	original	original	ADJ
gra-920	45	18	research	research	NOUN
gra-920	45	19	paper	paper	NOUN
gra-920	45	20	wenyan	wenyan	PROPN
gra-920	45	21	jiang	jiang	PROPN
gra-920	45	22	tiangong	tiangong	PROPN
gra-920	45	23	university	university	PROPN
gra-920	45	24	,	,	PUNCT
gra-920	45	25	no	no	INTJ
gra-920	45	26	.	.	NOUN
gra-920	45	27	399	399	NUM
gra-920	45	28	binshui	binshui	PROPN
gra-920	45	29	road	road	PROPN
gra-920	45	30	,	,	PUNCT
gra-920	45	31	xiqing	xiqing	PROPN
gra-920	45	32	district	district	PROPN
gra-920	45	33	.	.	PUNCT
gra-920	46	1	engineering	engineering	NOUN
gra-920	46	2	in	in	ADP
gra-920	46	3	the	the	DET
gra-920	46	4	research	research	NOUN
gra-920	46	5	of	of	ADP
gra-920	46	6	life	life	NOUN
gra-920	46	7	science	science	NOUN
gra-920	46	8	,	,	PUNCT
gra-920	46	9	bioinformatics	bioinformatic	NOUN
gra-920	46	10	has	have	AUX
gra-920	46	11	become	become	VERB
gra-920	46	12	the	the	DET
gra-920	46	13	main	main	ADJ
gra-920	46	14	direction	direction	NOUN
gra-920	46	15	of	of	ADP
gra-920	46	16	current	current	ADJ
gra-920	46	17	research	research	NOUN
gra-920	46	18	,	,	PUNCT
gra-920	46	19	mainly	mainly	ADV
gra-920	46	20	in	in	ADP
gra-920	46	21	the	the	DET
gra-920	46	22	aspects	aspect	NOUN
gra-920	46	23	of	of	ADP
gra-920	46	24	gene	gene	NOUN
gra-920	46	25	expression	expression	NOUN
gra-920	46	26	analysis	analysis	NOUN
gra-920	46	27	,	,	PUNCT
gra-920	46	28	genomics	genomics	NOUN
gra-920	46	29	and	and	CCONJ
gra-920	46	30	so	so	ADV
gra-920	46	31	on	on	ADV
gra-920	46	32	.	.	PUNCT
gra-920	47	1	with	with	ADP
gra-920	47	2	the	the	DET
gra-920	47	3	development	development	NOUN
gra-920	47	4	of	of	ADP
gra-920	47	5	the	the	DET
gra-920	47	6	rna	rna	NOUN
gra-920	47	7	-	-	PUNCT
gra-920	47	8	seq	seq	NOUN
gra-920	47	9	technique	technique	NOUN
gra-920	47	10	,	,	PUNCT
gra-920	47	11	we	we	PRON
gra-920	47	12	have	have	AUX
gra-920	47	13	made	make	VERB
gra-920	47	14	a	a	DET
gra-920	47	15	breakthrough	breakthrough	NOUN
gra-920	47	16	in	in	ADP
gra-920	47	17	the	the	DET
gra-920	47	18	biotechnology	biotechnology	NOUN
gra-920	47	19	,	,	PUNCT
gra-920	47	20	and	and	CCONJ
gra-920	47	21	on	on	ADP
gra-920	47	22	the	the	DET
gra-920	47	23	basis	basis	NOUN
gra-920	47	24	of	of	ADP
gra-920	47	25	the	the	DET
gra-920	47	26	expression	expression	NOUN
gra-920	47	27	level	level	NOUN
gra-920	47	28	of	of	ADP
gra-920	47	29	the	the	DET
gra-920	47	30	rna	rna	NOUN
gra-920	47	31	-	-	PUNCT
gra-920	47	32	seq	seq	NOUN
gra-920	47	33	data	datum	NOUN
gra-920	47	34	,	,	PUNCT
gra-920	47	35	the	the	DET
gra-920	47	36	data	data	NOUN
gra-920	47	37	is	be	AUX
gra-920	47	38	getting	get	VERB
gra-920	47	39	bigger	big	ADJ
gra-920	47	40	and	and	CCONJ
gra-920	47	41	more	more	ADV
gra-920	47	42	and	and	CCONJ
gra-920	47	43	more	more	ADV
gra-920	47	44	and	and	CCONJ
gra-920	47	45	more	more	ADV
gra-920	47	46	,	,	PUNCT
gra-920	47	47	and	and	CCONJ
gra-920	47	48	the	the	DET
gra-920	47	49	accuracy	accuracy	NOUN
gra-920	47	50	of	of	ADP
gra-920	47	51	the	the	DET
gra-920	47	52	gene	gene	NOUN
gra-920	47	53	expression	expression	NOUN
gra-920	47	54	level	level	NOUN
gra-920	47	55	is	be	AUX
gra-920	47	56	improved	improve	VERB
gra-920	47	57	by	by	ADP
gra-920	47	58	the	the	DET
gra-920	47	59	method	method	NOUN
gra-920	47	60	of	of	ADP
gra-920	47	61	clustering	clustering	NOUN
gra-920	47	62	.	.	PUNCT
gra-920	48	1	in	in	ADP
gra-920	48	2	this	this	DET
gra-920	48	3	paper	paper	NOUN
gra-920	48	4	,	,	PUNCT
gra-920	48	5	four	four	NUM
gra-920	48	6	clustering	clustering	ADJ
gra-920	48	7	algorithms	algorithm	NOUN
gra-920	48	8	are	be	AUX
gra-920	48	9	introduced	introduce	VERB
gra-920	48	10	,	,	PUNCT
gra-920	48	11	and	and	CCONJ
gra-920	48	12	the	the	DET
gra-920	48	13	clustering	clustering	ADJ
gra-920	48	14	algorithm	algorithm	NOUN
gra-920	48	15	with	with	ADP
gra-920	48	16	relatively	relatively	ADV
gra-920	48	17	good	good	ADJ
gra-920	48	18	performance	performance	NOUN
gra-920	48	19	is	be	AUX
gra-920	48	20	compared	compare	VERB
gra-920	48	21	.	.	PUNCT
gra-920	49	1	abstract	abstract	ADJ
gra-920	49	2	keywords	keyword	NOUN
gra-920	49	3	:	:	PUNCT
gra-920	49	4	rna	rna	NOUN
gra-920	49	5	-	-	PUNCT
gra-920	49	6	seq	seq	NOUN
gra-920	49	7	,	,	PUNCT
gra-920	49	8	clustering	clustering	NOUN
gra-920	49	9	,	,	PUNCT
gra-920	49	10	k	k	ADJ
gra-920	49	11	-	-	NOUN
gra-920	49	12	mean	mean	ADJ
gra-920	49	13	,	,	PUNCT
gra-920	49	14	louvain	louvain	NOUN
gra-920	49	15	volume-8	volume-8	NUM
gra-920	49	16	,	,	PUNCT
gra-920	49	17	issue-12	issue-12	NOUN
gra-920	49	18	,	,	PUNCT
gra-920	49	19	december-2019	december-2019	ADJ
gra-920	49	20	•	•	NUM
gra-920	49	21	print	print	NOUN
gra-920	49	22	issn	issn	PROPN
gra-920	49	23	no	no	NOUN
gra-920	49	24	.	.	PROPN
gra-920	49	25	2277	2277	NUM
gra-920	49	26	8160	8160	NUM
gra-920	49	27	•	•	NOUN
gra-920	49	28	doi	doi	NOUN
gra-920	49	29	:	:	PUNCT
gra-920	49	30	10.36106	10.36106	NUM
gra-920	49	31	/	/	SYM
gra-920	49	32	gjra	gjra	NOUN
gra-920	49	33	82	82	NUM
gra-920	49	34	x	x	SYM
gra-920	49	35	gjra	gjra	PROPN
gra-920	49	36	global	global	ADJ
gra-920	49	37	journal	journal	PROPN
gra-920	49	38	for	for	ADP
gra-920	49	39	research	research	NOUN
gra-920	49	40	analysis	analysis	NOUN
gra-920	49	41	until	until	SCONJ
gra-920	49	42	the	the	DET
gra-920	49	43	algorithm	algorithm	NOUN
gra-920	49	44	ends	end	VERB
gra-920	49	45	.	.	PUNCT
gra-920	50	1	compared	compare	VERB
gra-920	50	2	with	with	ADP
gra-920	50	3	other	other	ADJ
gra-920	50	4	algorithms	algorithm	NOUN
gra-920	50	5	,	,	PUNCT
gra-920	50	6	this	this	DET
gra-920	50	7	algorithm	algorithm	NOUN
gra-920	50	8	is	be	AUX
gra-920	50	9	relatively	relatively	ADV
gra-920	50	10	easy	easy	ADJ
gra-920	50	11	to	to	PART
gra-920	50	12	implement	implement	VERB
gra-920	50	13	,	,	PUNCT
gra-920	50	14	and	and	CCONJ
gra-920	50	15	the	the	DET
gra-920	50	16	results	result	NOUN
gra-920	50	17	are	be	AUX
gra-920	50	18	unsupervised	unsupervised	ADJ
gra-920	50	19	.	.	PUNCT
gra-920	51	1	this	this	DET
gra-920	51	2	algorithm	algorithm	NOUN
gra-920	51	3	is	be	AUX
gra-920	51	4	very	very	ADV
gra-920	51	5	fast	fast	ADJ
gra-920	51	6	and	and	CCONJ
gra-920	51	7	the	the	DET
gra-920	51	8	complexity	complexity	NOUN
gra-920	51	9	of	of	ADP
gra-920	51	10	typical	typical	ADJ
gra-920	51	11	sparse	sparse	ADJ
gra-920	51	12	data	datum	NOUN
gra-920	51	13	is	be	AUX
gra-920	51	14	linear	linear	ADJ
gra-920	51	15	.	.	PUNCT
gra-920	52	1	after	after	ADP
gra-920	52	2	several	several	ADJ
gra-920	52	3	iterations	iteration	NOUN
gra-920	52	4	,	,	PUNCT
gra-920	52	5	the	the	DET
gra-920	52	6	data	datum	NOUN
gra-920	52	7	will	will	AUX
gra-920	52	8	become	become	VERB
gra-920	52	9	less	less	ADV
gra-920	52	10	sharply	sharply	ADV
gra-920	52	11	,	,	PUNCT
gra-920	52	12	so	so	SCONJ
gra-920	52	13	the	the	DET
gra-920	52	14	time	time	NOUN
gra-920	52	15	of	of	ADP
gra-920	52	16	this	this	DET
gra-920	52	17	algorithm	algorithm	NOUN
gra-920	52	18	is	be	AUX
gra-920	52	19	mainly	mainly	ADV
gra-920	52	20	consumed	consume	VERB
gra-920	52	21	in	in	ADP
gra-920	52	22	the	the	DET
gra-920	52	23	rst	rst	PROPN
gra-920	52	24	iteration	iteration	NOUN
gra-920	52	25	.	.	PUNCT
gra-920	53	1	3.conclusions	3.conclusions	NUM
gra-920	53	2	these	these	DET
gra-920	53	3	traditional	traditional	ADJ
gra-920	53	4	clustering	clustering	ADJ
gra-920	53	5	methods	method	NOUN
gra-920	53	6	are	be	AUX
gra-920	53	7	based	base	VERB
gra-920	53	8	on	on	ADP
gra-920	53	9	heuristic	heuristic	ADJ
gra-920	53	10	algorithm	algorithm	NOUN
gra-920	53	11	,	,	PUNCT
gra-920	53	12	which	which	PRON
gra-920	53	13	is	be	AUX
gra-920	53	14	difcult	difcult	VERB
gra-920	53	15	to	to	PART
gra-920	53	16	compare	compare	VERB
gra-920	53	17	the	the	DET
gra-920	53	18	advantages	advantage	NOUN
gra-920	53	19	and	and	CCONJ
gra-920	53	20	disadvantages	disadvantage	NOUN
gra-920	53	21	of	of	ADP
gra-920	53	22	various	various	ADJ
gra-920	53	23	algorithms	algorithm	NOUN
gra-920	53	24	.	.	PUNCT
gra-920	54	1	these	these	DET
gra-920	54	2	algorithms	algorithm	NOUN
gra-920	54	3	can	can	AUX
gra-920	54	4	get	get	VERB
gra-920	54	5	better	well	ADJ
gra-920	54	6	clustering	clustering	ADJ
gra-920	54	7	results	result	NOUN
gra-920	54	8	when	when	SCONJ
gra-920	54	9	applied	apply	VERB
gra-920	54	10	to	to	ADP
gra-920	54	11	rna	rna	NOUN
gra-920	54	12	-	-	PUNCT
gra-920	54	13	seq	seq	NOUN
gra-920	54	14	data	datum	NOUN
gra-920	54	15	.	.	PUNCT
gra-920	55	1	these	these	DET
gra-920	55	2	methods	method	NOUN
gra-920	55	3	basically	basically	ADV
gra-920	55	4	use	use	VERB
gra-920	55	5	poisson	poisson	NOUN
gra-920	55	6	distribution	distribution	NOUN
gra-920	55	7	to	to	PART
gra-920	55	8	model	model	VERB
gra-920	55	9	rna	rna	PROPN
gra-920	55	10	-	-	PUNCT
gra-920	55	11	seq	seq	NOUN
gra-920	55	12	data	datum	NOUN
gra-920	55	13	,	,	PUNCT
gra-920	55	14	but	but	CCONJ
gra-920	55	15	there	there	PRON
gra-920	55	16	are	be	VERB
gra-920	55	17	some	some	DET
gra-920	55	18	differences	difference	NOUN
gra-920	55	19	between	between	ADP
gra-920	55	20	these	these	DET
gra-920	55	21	data	datum	NOUN
gra-920	55	22	and	and	CCONJ
gra-920	55	23	poisson	poisson	NOUN
gra-920	55	24	model	model	NOUN
gra-920	56	1	[	[	X
gra-920	56	2	9][10	9][10	NUM
gra-920	56	3	]	]	PUNCT
gra-920	56	4	,	,	PUNCT
gra-920	56	5	and	and	CCONJ
gra-920	56	6	there	there	PRON
gra-920	56	7	is	be	VERB
gra-920	56	8	no	no	DET
gra-920	56	9	specic	specic	NOUN
gra-920	56	10	principle	principle	NOUN
gra-920	56	11	to	to	PART
gra-920	56	12	determine	determine	VERB
gra-920	56	13	the	the	DET
gra-920	56	14	optimal	optimal	ADJ
gra-920	56	15	number	number	NOUN
gra-920	56	16	of	of	ADP
gra-920	56	17	classes	class	NOUN
gra-920	56	18	,	,	PUNCT
gra-920	56	19	so	so	CCONJ
gra-920	56	20	it	it	PRON
gra-920	56	21	is	be	AUX
gra-920	56	22	also	also	ADV
gra-920	56	23	a	a	DET
gra-920	56	24	problem	problem	NOUN
gra-920	56	25	to	to	PART
gra-920	56	26	be	be	AUX
gra-920	56	27	solved	solve	VERB
gra-920	56	28	in	in	ADP
gra-920	56	29	the	the	DET
gra-920	56	30	future	future	NOUN
gra-920	56	31	to	to	PART
gra-920	56	32	propose	propose	VERB
gra-920	56	33	a	a	DET
gra-920	56	34	better	well	ADJ
gra-920	56	35	algorithm	algorithm	NOUN
gra-920	56	36	for	for	ADP
gra-920	56	37	clustering	clustering	NOUN
gra-920	56	38	.	.	PUNCT
gra-920	57	1	references	reference	NOUN
gra-920	57	2	:	:	PUNCT
gra-920	58	1	[	[	X
gra-920	58	2	1	1	X
gra-920	58	3	]	]	X
gra-920	58	4	wang	wang	PROPN
gra-920	58	5	z	z	PROPN
gra-920	58	6	,	,	PUNCT
gra-920	58	7	gerstein	gerstein	PROPN
gra-920	58	8	m	m	PROPN
gra-920	58	9	,	,	PUNCT
gra-920	58	10	snyder	snyder	PROPN
gra-920	58	11	m.	m.	PROPN
gra-920	58	12	rna	rna	PROPN
gra-920	58	13	-	-	PUNCT
gra-920	58	14	seq	seq	NOUN
gra-920	58	15	:	:	PUNCT
gra-920	58	16	a	a	DET
gra-920	58	17	revolutionary	revolutionary	ADJ
gra-920	58	18	tool	tool	NOUN
gra-920	58	19	for	for	ADP
gra-920	58	20	transcriptomics[j	transcriptomics[j	PROPN
gra-920	58	21	]	]	PUNCT
gra-920	58	22	.	.	PUNCT
gra-920	59	1	nature	nature	NOUN
gra-920	59	2	reviews	review	VERB
gra-920	59	3	genetics	genetic	NOUN
gra-920	59	4	,	,	PUNCT
gra-920	59	5	2009	2009	NUM
gra-920	59	6	,	,	PUNCT
gra-920	59	7	10(1	10(1	NUM
gra-920	59	8	):	):	PUNCT
gra-920	59	9	57	57	NUM
gra-920	59	10	.	.	PUNCT
gra-920	60	1	[	[	X
gra-920	60	2	2	2	NUM
gra-920	60	3	]	]	SYM
gra-920	60	4	pachter	pachter	PROPN
gra-920	60	5	l.	l.	PROPN
gra-920	60	6	models	model	NOUN
gra-920	60	7	for	for	ADP
gra-920	60	8	transcript	transcript	NOUN
gra-920	60	9	quantication	quantication	NOUN
gra-920	60	10	from	from	ADP
gra-920	60	11	rna	rna	NOUN
gra-920	60	12	-	-	PUNCT
gra-920	60	13	seq[j	seq[j	NOUN
gra-920	60	14	]	]	PUNCT
gra-920	60	15	.	.	PUNCT
gra-920	61	1	arxiv	arxiv	PROPN
gra-920	61	2	preprint	preprint	PROPN
gra-920	61	3	arxiv:1104.3889	arxiv:1104.3889	PROPN
gra-920	61	4	,	,	PUNCT
gra-920	61	5	2011	2011	NUM
gra-920	61	6	.	.	PUNCT
gra-920	62	1	[	[	X
gra-920	62	2	3	3	NUM
gra-920	62	3	]	]	PUNCT
gra-920	62	4	marioni	marioni	NOUN
gra-920	62	5	j	j	PROPN
gra-920	62	6	c	c	PROPN
gra-920	62	7	,	,	PUNCT
gra-920	62	8	mason	mason	PROPN
gra-920	62	9	c	c	PROPN
gra-920	62	10	e	e	PROPN
gra-920	62	11	,	,	PUNCT
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gra-920	62	13	s	s	NOUN
gra-920	62	14	m	m	PROPN
gra-920	62	15	,	,	PUNCT
gra-920	62	16	et	et	PROPN
gra-920	62	17	al	al	PROPN
gra-920	62	18	.	.	PUNCT
gra-920	63	1	rna	rna	PROPN
gra-920	63	2	-	-	PUNCT
gra-920	63	3	seq	seq	NOUN
gra-920	63	4	:	:	PUNCT
gra-920	63	5	an	an	DET
gra-920	63	6	assessment	assessment	NOUN
gra-920	63	7	of	of	ADP
gra-920	63	8	technical	technical	ADJ
gra-920	63	9	reproducibility	reproducibility	NOUN
gra-920	63	10	and	and	CCONJ
gra-920	63	11	comparison	comparison	NOUN
gra-920	63	12	with	with	ADP
gra-920	63	13	gene	gene	NOUN
gra-920	63	14	expression	expression	NOUN
gra-920	63	15	arrays[j	arrays[j	NOUN
gra-920	63	16	]	]	PUNCT
gra-920	63	17	.	.	PUNCT
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gra-920	64	2	research	research	NOUN
gra-920	64	3	,	,	PUNCT
gra-920	64	4	2008	2008	NUM
gra-920	64	5	,	,	PUNCT
gra-920	64	6	18(9	18(9	NUM
gra-920	64	7	):	):	PUNCT
gra-920	64	8	1509	1509	NUM
gra-920	64	9	-	-	SYM
gra-920	64	10	1517	1517	NUM
gra-920	64	11	.	.	PUNCT
gra-920	65	1	[	[	X
gra-920	65	2	4	4	X
gra-920	65	3	]	]	PUNCT
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gra-920	65	5	a	a	PROPN
gra-920	65	6	,	,	PUNCT
gra-920	65	7	sharma	sharma	PROPN
gra-920	65	8	s.	s.	PROPN
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gra-920	65	10	of	of	ADP
gra-920	65	11	rock	rock	NOUN
gra-920	65	12	clustering	cluster	VERB
gra-920	65	13	algorithm	algorithm	NOUN
gra-920	65	14	for	for	ADP
gra-920	65	15	the	the	DET
gra-920	65	16	optimization	optimization	NOUN
gra-920	65	17	of	of	ADP
gra-920	65	18	query	query	NOUN
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gra-920	65	20	time[j	time[j	PROPN
gra-920	65	21	]	]	PUNCT
gra-920	65	22	.	.	PUNCT
gra-920	66	1	international	international	ADJ
gra-920	66	2	journal	journal	PROPN
gra-920	66	3	on	on	ADP
gra-920	66	4	computer	computer	NOUN
gra-920	66	5	science	science	NOUN
gra-920	66	6	and	and	CCONJ
gra-920	66	7	engineering	engineering	NOUN
gra-920	66	8	,	,	PUNCT
gra-920	66	9	2012	2012	NUM
gra-920	66	10	,	,	PUNCT
gra-920	66	11	4(5	4(5	NUM
gra-920	66	12	):	):	PUNCT
gra-920	66	13	809	809	NUM
gra-920	66	14	.	.	PUNCT
gra-920	67	1	[	[	X
gra-920	67	2	5	5	NUM
gra-920	67	3	]	]	PUNCT
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gra-920	67	5	t	t	PROPN
gra-920	67	6	,	,	PUNCT
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gra-920	67	8	r	r	NOUN
gra-920	67	9	,	,	PUNCT
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gra-920	67	11	m.	m.	NOUN
gra-920	67	12	birch	birch	PROPN
gra-920	67	13	:	:	PUNCT
gra-920	67	14	an	an	DET
gra-920	67	15	efcient	efcient	ADJ
gra-920	67	16	data	datum	NOUN
gra-920	67	17	clustering	cluster	VERB
gra-920	67	18	method	method	NOUN
gra-920	67	19	for	for	ADP
gra-920	67	20	very	very	ADV
gra-920	67	21	large	large	ADJ
gra-920	67	22	databases[c]//acm	databases[c]//acm	NOUN
gra-920	67	23	sigmod	sigmod	NOUN
gra-920	67	24	record	record	NOUN
gra-920	67	25	.	.	PUNCT
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gra-920	67	27	,	,	PUNCT
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gra-920	67	29	,	,	PUNCT
gra-920	67	30	25(2	25(2	NUM
gra-920	67	31	):	):	PUNCT
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gra-920	67	33	-	-	SYM
gra-920	67	34	114	114	NUM
gra-920	67	35	.	.	PUNCT
gra-920	68	1	[	[	X
gra-920	68	2	6	6	NUM
gra-920	68	3	]	]	PUNCT
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gra-920	68	5	p	p	X
gra-920	68	6	,	,	PUNCT
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gra-920	68	8	d	d	PROPN
gra-920	68	9	,	,	PUNCT
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gra-920	68	11	j	j	PROPN
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gra-920	68	18	of	of	ADP
gra-920	68	19	gene	gene	NOUN
gra-920	68	20	expression	expression	NOUN
gra-920	68	21	with	with	ADP
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gra-920	68	23	-	-	PUNCT
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gra-920	68	28	and	and	CCONJ
gra-920	68	29	application	application	NOUN
gra-920	68	30	to	to	ADP
gra-920	68	31	hematopoietic	hematopoietic	ADJ
gra-920	68	32	differentiation[j	differentiation[j	PROPN
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gra-920	68	34	.	.	PUNCT
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gra-920	69	2	of	of	ADP
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gra-920	69	6	of	of	ADP
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gra-920	69	8	,	,	PUNCT
gra-920	69	9	1999	1999	NUM
gra-920	69	10	,	,	PUNCT
gra-920	69	11	96(6	96(6	NUM
gra-920	69	12	):	):	PUNCT
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gra-920	69	14	-	-	SYM
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gra-920	69	16	.	.	PUNCT
gra-920	70	1	[	[	X
gra-920	70	2	7	7	X
gra-920	70	3	]	]	X
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gra-920	70	5	s	s	PROPN
gra-920	70	6	,	,	PUNCT
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gra-920	70	9	,	,	PUNCT
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gra-920	70	13	:	:	PUNCT
gra-920	70	14	a	a	DET
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gra-920	70	16	clustering	clustering	ADJ
gra-920	70	17	algorithm	algorithm	NOUN
gra-920	70	18	for	for	ADP
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gra-920	70	20	attributes[j	attributes[j	PROPN
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gra-920	70	22	.	.	PUNCT
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gra-920	71	2	systems	system	NOUN
gra-920	71	3	,	,	PUNCT
gra-920	71	4	2000	2000	NUM
gra-920	71	5	,	,	PUNCT
gra-920	71	6	25(5	25(5	NUM
gra-920	71	7	):	):	PUNCT
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gra-920	71	9	-	-	SYM
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gra-920	71	11	.	.	PUNCT
gra-920	72	1	[	[	X
gra-920	72	2	8	8	NUM
gra-920	72	3	]	]	PUNCT
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gra-920	72	7	,	,	PUNCT
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gra-920	72	11	,	,	PUNCT
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gra-920	72	13	r	r	NOUN
gra-920	72	14	,	,	PUNCT
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gra-920	72	16	al	al	PROPN
gra-920	72	17	.	.	PUNCT
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gra-920	72	22	in	in	ADP
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gra-920	72	26	.	.	PUNCT
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gra-920	73	5	:	:	PUNCT
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gra-920	73	7	and	and	CCONJ
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gra-920	73	9	,	,	PUNCT
gra-920	73	10	2008	2008	NUM
gra-920	73	11	,	,	PUNCT
gra-920	73	12	2008(10	2008(10	NUM
gra-920	73	13	):	):	PUNCT
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gra-920	73	15	.	.	PUNCT
gra-920	74	1	[	[	X
gra-920	74	2	9	9	NUM
gra-920	74	3	]	]	PUNCT
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gra-920	74	5	d	d	X
gra-920	74	6	m.	m.	NOUN
gra-920	74	7	cla	cla	PROPN
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gra-920	74	9	and	and	CCONJ
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gra-920	74	13	sequencing	sequence	VERB
gra-920	74	14	data	datum	NOUN
gra-920	74	15	using	use	VERB
gra-920	74	16	a	a	DET
gra-920	74	17	poisson	poisson	NOUN
gra-920	74	18	model[j	model[j	PROPN
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gra-920	74	20	.	.	PUNCT
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gra-920	75	2	of	of	ADP
gra-920	75	3	applied	applied	ADJ
gra-920	75	4	statistics	statistic	NOUN
gra-920	75	5	,	,	PUNCT
gra-920	75	6	2011	2011	NUM
gra-920	75	7	,	,	PUNCT
gra-920	75	8	5(4	5(4	NUM
gra-920	75	9	):	):	PUNCT
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gra-920	75	12	.	.	PUNCT
gra-920	76	1	[	[	X
gra-920	76	2	10	10	NUM
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gra-920	76	4	wang	wang	PROPN
gra-920	76	5	n	n	PROPN
gra-920	76	6	,	,	PUNCT
gra-920	76	7	wang	wang	PROPN
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gra-920	76	9	,	,	PUNCT
gra-920	76	10	han	han	PROPN
gra-920	76	11	h	h	PROPN
gra-920	76	12	,	,	PUNCT
gra-920	76	13	et	et	PROPN
gra-920	76	14	a	a	DET
gra-920	76	15	l.	l.	PROPN
gra-920	76	16	a	a	DET
gra-920	76	17	b	b	PROPN
gra-920	76	18	i	i	PRON
gra-920	76	19	-	-	PUNCT
gra-920	76	20	poisson	poisson	PROPN
gra-920	76	21	model	model	NOUN
gra-920	76	22	for	for	ADP
gra-920	76	23	c	c	NOUN
gra-920	76	24	lustering	lustere	VERB
gra-920	76	25	gene	gene	NOUN
gra-920	76	26	expression	expression	NOUN
gra-920	76	27	proles	prole	NOUN
gra-920	76	28	by	by	ADP
gra-920	76	29	rna	rna	NOUN
gra-920	76	30	-	-	PUNCT
gra-920	76	31	seq[j	seq[j	NOUN
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gra-920	76	33	.	.	PUNCT
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gra-920	77	3	in	in	ADP
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gra-920	77	5	informatics	informatics	PROPN
gra-920	77	6	,	,	PUNCT
gra-920	77	7	2013	2013	NUM
gra-920	77	8	,	,	PUNCT
gra-920	77	9	15(4	15(4	NUM
gra-920	77	10	):	):	PUNCT
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gra-920	77	12	-	-	SYM
gra-920	77	13	541	541	NUM
gra-920	77	14	.	.	PUNCT
gra-920	78	1	volume-8	volume-8	NUM
gra-920	78	2	,	,	PUNCT
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gra-920	78	4	,	,	PUNCT
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gra-920	78	10	.	.	PROPN
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gra-920	79	2	8160	8160	NUM
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gra-920	79	5	:	:	PUNCT
gra-920	79	6	10.36106	10.36106	NUM
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