scGRNom: a computational pipeline of integrative multi-omics analyses for predicting cell-type disease genes and regulatory networks

Abstract Understanding cell-type-specific gene regulatory mechanisms from genetic variants to diseases remains challenging. To address this, we developed a computational pipeline, scGRNom (single-cell Gene Regulatory Network prediction from multi-omics), to predict cell-type disease genes and regula...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Ting Jin [verfasserIn] Peter Rehani [verfasserIn] Mufang Ying [verfasserIn] Jiawei Huang [verfasserIn] Shuang Liu [verfasserIn] Panagiotis Roussos [verfasserIn] Daifeng Wang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Single-cell genomics Single-cell multi-omics integration Cell-type gene regulatory network Cell-type disease risk genes Cross-disease functional genomics Schizophrenia

Übergeordnetes Werk:	In: Genome Medicine - BMC, 2016, 13(2021), 1, Seite 15
Übergeordnetes Werk:	volume:13 ; year:2021 ; number:1 ; pages:15

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/s13073-021-00908-9

Katalog-ID:	DOAJ054999243

Internformat


LEADER	01000caa a22002652 4500
001	DOAJ054999243
003	DE-627
005	20230308185152.0
007	cr uuu---uuuuu
008	230227s2021 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1186/s13073-021-00908-9 \|2 doi
035			\|a (DE-627)DOAJ054999243
035			\|a (DE-599)DOAJa05e48828d364f5a92653c3fea92879d
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
050		0	\|a QH426-470
100	0		\|a Ting Jin \|e verfasserin \|4 aut
245	1	0	\|a scGRNom: a computational pipeline of integrative multi-omics analyses for predicting cell-type disease genes and regulatory networks
264		1	\|c 2021
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a Abstract Understanding cell-type-specific gene regulatory mechanisms from genetic variants to diseases remains challenging. To address this, we developed a computational pipeline, scGRNom (single-cell Gene Regulatory Network prediction from multi-omics), to predict cell-type disease genes and regulatory networks including transcription factors and regulatory elements. With applications to schizophrenia and Alzheimer’s disease, we predicted disease genes and regulatory networks for excitatory and inhibitory neurons, microglia, and oligodendrocytes. Further enrichment analyses revealed cross-disease and disease-specific functions and pathways at the cell-type level. Our machine learning analysis also found that cell-type disease genes improved clinical phenotype predictions. scGRNom is a general-purpose tool available at https://github.com/daifengwanglab/scGRNom .
650		4	\|a Single-cell genomics
650		4	\|a Single-cell multi-omics integration
650		4	\|a Cell-type gene regulatory network
650		4	\|a Cell-type disease risk genes
650		4	\|a Cross-disease functional genomics
650		4	\|a Schizophrenia
653		0	\|a Medicine
653		0	\|a R
653		0	\|a Genetics
700	0		\|a Peter Rehani \|e verfasserin \|4 aut
700	0		\|a Mufang Ying \|e verfasserin \|4 aut
700	0		\|a Jiawei Huang \|e verfasserin \|4 aut
700	0		\|a Shuang Liu \|e verfasserin \|4 aut
700	0		\|a Panagiotis Roussos \|e verfasserin \|4 aut
700	0		\|a Daifeng Wang \|e verfasserin \|4 aut
773	0	8	\|i In \|t Genome Medicine \|d BMC, 2016 \|g 13(2021), 1, Seite 15 \|w (DE-627)594424275 \|w (DE-600)2484394-5 \|x 1756994X \|7 nnns
773	1	8	\|g volume:13 \|g year:2021 \|g number:1 \|g pages:15
856	4	0	\|u https://doi.org/10.1186/s13073-021-00908-9 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/a05e48828d364f5a92653c3fea92879d \|z kostenfrei
856	4	0	\|u https://doi.org/10.1186/s13073-021-00908-9 \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/1756-994X \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a GBV_ILN_11
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_206
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 13 \|j 2021 \|e 1 \|h 15

Indexfelder

author_variant	t j tj p r pr m y my j h jh s l sl p r pr d w dw
matchkey_str	article:1756994X:2021----::croaopttoapplnoitgaieutoisnlssopeitncltpds
hierarchy_sort_str	2021
callnumber-subject-code	QH
publishDate	2021
allfields	10.1186/s13073-021-00908-9 doi (DE-627)DOAJ054999243 (DE-599)DOAJa05e48828d364f5a92653c3fea92879d DE-627 ger DE-627 rakwb eng QH426-470 Ting Jin verfasserin aut scGRNom: a computational pipeline of integrative multi-omics analyses for predicting cell-type disease genes and regulatory networks 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Understanding cell-type-specific gene regulatory mechanisms from genetic variants to diseases remains challenging. To address this, we developed a computational pipeline, scGRNom (single-cell Gene Regulatory Network prediction from multi-omics), to predict cell-type disease genes and regulatory networks including transcription factors and regulatory elements. With applications to schizophrenia and Alzheimer’s disease, we predicted disease genes and regulatory networks for excitatory and inhibitory neurons, microglia, and oligodendrocytes. Further enrichment analyses revealed cross-disease and disease-specific functions and pathways at the cell-type level. Our machine learning analysis also found that cell-type disease genes improved clinical phenotype predictions. scGRNom is a general-purpose tool available at https://github.com/daifengwanglab/scGRNom . Single-cell genomics Single-cell multi-omics integration Cell-type gene regulatory network Cell-type disease risk genes Cross-disease functional genomics Schizophrenia Medicine R Genetics Peter Rehani verfasserin aut Mufang Ying verfasserin aut Jiawei Huang verfasserin aut Shuang Liu verfasserin aut Panagiotis Roussos verfasserin aut Daifeng Wang verfasserin aut In Genome Medicine BMC, 2016 13(2021), 1, Seite 15 (DE-627)594424275 (DE-600)2484394-5 1756994X nnns volume:13 year:2021 number:1 pages:15 https://doi.org/10.1186/s13073-021-00908-9 kostenfrei https://doaj.org/article/a05e48828d364f5a92653c3fea92879d kostenfrei https://doi.org/10.1186/s13073-021-00908-9 kostenfrei https://doaj.org/toc/1756-994X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2021 1 15
spelling	10.1186/s13073-021-00908-9 doi (DE-627)DOAJ054999243 (DE-599)DOAJa05e48828d364f5a92653c3fea92879d DE-627 ger DE-627 rakwb eng QH426-470 Ting Jin verfasserin aut scGRNom: a computational pipeline of integrative multi-omics analyses for predicting cell-type disease genes and regulatory networks 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Understanding cell-type-specific gene regulatory mechanisms from genetic variants to diseases remains challenging. To address this, we developed a computational pipeline, scGRNom (single-cell Gene Regulatory Network prediction from multi-omics), to predict cell-type disease genes and regulatory networks including transcription factors and regulatory elements. With applications to schizophrenia and Alzheimer’s disease, we predicted disease genes and regulatory networks for excitatory and inhibitory neurons, microglia, and oligodendrocytes. Further enrichment analyses revealed cross-disease and disease-specific functions and pathways at the cell-type level. Our machine learning analysis also found that cell-type disease genes improved clinical phenotype predictions. scGRNom is a general-purpose tool available at https://github.com/daifengwanglab/scGRNom . Single-cell genomics Single-cell multi-omics integration Cell-type gene regulatory network Cell-type disease risk genes Cross-disease functional genomics Schizophrenia Medicine R Genetics Peter Rehani verfasserin aut Mufang Ying verfasserin aut Jiawei Huang verfasserin aut Shuang Liu verfasserin aut Panagiotis Roussos verfasserin aut Daifeng Wang verfasserin aut In Genome Medicine BMC, 2016 13(2021), 1, Seite 15 (DE-627)594424275 (DE-600)2484394-5 1756994X nnns volume:13 year:2021 number:1 pages:15 https://doi.org/10.1186/s13073-021-00908-9 kostenfrei https://doaj.org/article/a05e48828d364f5a92653c3fea92879d kostenfrei https://doi.org/10.1186/s13073-021-00908-9 kostenfrei https://doaj.org/toc/1756-994X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2021 1 15
allfields_unstemmed	10.1186/s13073-021-00908-9 doi (DE-627)DOAJ054999243 (DE-599)DOAJa05e48828d364f5a92653c3fea92879d DE-627 ger DE-627 rakwb eng QH426-470 Ting Jin verfasserin aut scGRNom: a computational pipeline of integrative multi-omics analyses for predicting cell-type disease genes and regulatory networks 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Understanding cell-type-specific gene regulatory mechanisms from genetic variants to diseases remains challenging. To address this, we developed a computational pipeline, scGRNom (single-cell Gene Regulatory Network prediction from multi-omics), to predict cell-type disease genes and regulatory networks including transcription factors and regulatory elements. With applications to schizophrenia and Alzheimer’s disease, we predicted disease genes and regulatory networks for excitatory and inhibitory neurons, microglia, and oligodendrocytes. Further enrichment analyses revealed cross-disease and disease-specific functions and pathways at the cell-type level. Our machine learning analysis also found that cell-type disease genes improved clinical phenotype predictions. scGRNom is a general-purpose tool available at https://github.com/daifengwanglab/scGRNom . Single-cell genomics Single-cell multi-omics integration Cell-type gene regulatory network Cell-type disease risk genes Cross-disease functional genomics Schizophrenia Medicine R Genetics Peter Rehani verfasserin aut Mufang Ying verfasserin aut Jiawei Huang verfasserin aut Shuang Liu verfasserin aut Panagiotis Roussos verfasserin aut Daifeng Wang verfasserin aut In Genome Medicine BMC, 2016 13(2021), 1, Seite 15 (DE-627)594424275 (DE-600)2484394-5 1756994X nnns volume:13 year:2021 number:1 pages:15 https://doi.org/10.1186/s13073-021-00908-9 kostenfrei https://doaj.org/article/a05e48828d364f5a92653c3fea92879d kostenfrei https://doi.org/10.1186/s13073-021-00908-9 kostenfrei https://doaj.org/toc/1756-994X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2021 1 15
allfieldsGer	10.1186/s13073-021-00908-9 doi (DE-627)DOAJ054999243 (DE-599)DOAJa05e48828d364f5a92653c3fea92879d DE-627 ger DE-627 rakwb eng QH426-470 Ting Jin verfasserin aut scGRNom: a computational pipeline of integrative multi-omics analyses for predicting cell-type disease genes and regulatory networks 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Understanding cell-type-specific gene regulatory mechanisms from genetic variants to diseases remains challenging. To address this, we developed a computational pipeline, scGRNom (single-cell Gene Regulatory Network prediction from multi-omics), to predict cell-type disease genes and regulatory networks including transcription factors and regulatory elements. With applications to schizophrenia and Alzheimer’s disease, we predicted disease genes and regulatory networks for excitatory and inhibitory neurons, microglia, and oligodendrocytes. Further enrichment analyses revealed cross-disease and disease-specific functions and pathways at the cell-type level. Our machine learning analysis also found that cell-type disease genes improved clinical phenotype predictions. scGRNom is a general-purpose tool available at https://github.com/daifengwanglab/scGRNom . Single-cell genomics Single-cell multi-omics integration Cell-type gene regulatory network Cell-type disease risk genes Cross-disease functional genomics Schizophrenia Medicine R Genetics Peter Rehani verfasserin aut Mufang Ying verfasserin aut Jiawei Huang verfasserin aut Shuang Liu verfasserin aut Panagiotis Roussos verfasserin aut Daifeng Wang verfasserin aut In Genome Medicine BMC, 2016 13(2021), 1, Seite 15 (DE-627)594424275 (DE-600)2484394-5 1756994X nnns volume:13 year:2021 number:1 pages:15 https://doi.org/10.1186/s13073-021-00908-9 kostenfrei https://doaj.org/article/a05e48828d364f5a92653c3fea92879d kostenfrei https://doi.org/10.1186/s13073-021-00908-9 kostenfrei https://doaj.org/toc/1756-994X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2021 1 15
allfieldsSound	10.1186/s13073-021-00908-9 doi (DE-627)DOAJ054999243 (DE-599)DOAJa05e48828d364f5a92653c3fea92879d DE-627 ger DE-627 rakwb eng QH426-470 Ting Jin verfasserin aut scGRNom: a computational pipeline of integrative multi-omics analyses for predicting cell-type disease genes and regulatory networks 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Understanding cell-type-specific gene regulatory mechanisms from genetic variants to diseases remains challenging. To address this, we developed a computational pipeline, scGRNom (single-cell Gene Regulatory Network prediction from multi-omics), to predict cell-type disease genes and regulatory networks including transcription factors and regulatory elements. With applications to schizophrenia and Alzheimer’s disease, we predicted disease genes and regulatory networks for excitatory and inhibitory neurons, microglia, and oligodendrocytes. Further enrichment analyses revealed cross-disease and disease-specific functions and pathways at the cell-type level. Our machine learning analysis also found that cell-type disease genes improved clinical phenotype predictions. scGRNom is a general-purpose tool available at https://github.com/daifengwanglab/scGRNom . Single-cell genomics Single-cell multi-omics integration Cell-type gene regulatory network Cell-type disease risk genes Cross-disease functional genomics Schizophrenia Medicine R Genetics Peter Rehani verfasserin aut Mufang Ying verfasserin aut Jiawei Huang verfasserin aut Shuang Liu verfasserin aut Panagiotis Roussos verfasserin aut Daifeng Wang verfasserin aut In Genome Medicine BMC, 2016 13(2021), 1, Seite 15 (DE-627)594424275 (DE-600)2484394-5 1756994X nnns volume:13 year:2021 number:1 pages:15 https://doi.org/10.1186/s13073-021-00908-9 kostenfrei https://doaj.org/article/a05e48828d364f5a92653c3fea92879d kostenfrei https://doi.org/10.1186/s13073-021-00908-9 kostenfrei https://doaj.org/toc/1756-994X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2021 1 15
language	English
source	In Genome Medicine 13(2021), 1, Seite 15 volume:13 year:2021 number:1 pages:15
sourceStr	In Genome Medicine 13(2021), 1, Seite 15 volume:13 year:2021 number:1 pages:15
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Single-cell genomics Single-cell multi-omics integration Cell-type gene regulatory network Cell-type disease risk genes Cross-disease functional genomics Schizophrenia Medicine R Genetics
isfreeaccess_bool	true
container_title	Genome Medicine
authorswithroles_txt_mv	Ting Jin @@aut@@ Peter Rehani @@aut@@ Mufang Ying @@aut@@ Jiawei Huang @@aut@@ Shuang Liu @@aut@@ Panagiotis Roussos @@aut@@ Daifeng Wang @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	594424275
id	DOAJ054999243
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ054999243</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230308185152.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2021 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/s13073-021-00908-9</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ054999243</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJa05e48828d364f5a92653c3fea92879d</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QH426-470</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Ting Jin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">scGRNom: a computational pipeline of integrative multi-omics analyses for predicting cell-type disease genes and regulatory networks</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Understanding cell-type-specific gene regulatory mechanisms from genetic variants to diseases remains challenging. To address this, we developed a computational pipeline, scGRNom (single-cell Gene Regulatory Network prediction from multi-omics), to predict cell-type disease genes and regulatory networks including transcription factors and regulatory elements. With applications to schizophrenia and Alzheimer’s disease, we predicted disease genes and regulatory networks for excitatory and inhibitory neurons, microglia, and oligodendrocytes. Further enrichment analyses revealed cross-disease and disease-specific functions and pathways at the cell-type level. Our machine learning analysis also found that cell-type disease genes improved clinical phenotype predictions. scGRNom is a general-purpose tool available at https://github.com/daifengwanglab/scGRNom .</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Single-cell genomics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Single-cell multi-omics integration</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Cell-type gene regulatory network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Cell-type disease risk genes</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Cross-disease functional genomics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Schizophrenia</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Medicine</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">R</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Genetics</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Peter Rehani</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Mufang Ying</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jiawei Huang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Shuang Liu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Panagiotis Roussos</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Daifeng Wang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Genome Medicine</subfield><subfield code="d">BMC, 2016</subfield><subfield code="g">13(2021), 1, Seite 15</subfield><subfield code="w">(DE-627)594424275</subfield><subfield code="w">(DE-600)2484394-5</subfield><subfield code="x">1756994X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:13</subfield><subfield code="g">year:2021</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:15</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1186/s13073-021-00908-9</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/a05e48828d364f5a92653c3fea92879d</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1186/s13073-021-00908-9</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1756-994X</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">13</subfield><subfield code="j">2021</subfield><subfield code="e">1</subfield><subfield code="h">15</subfield></datafield></record></collection>
callnumber-first	Q - Science
author	Ting Jin
spellingShingle	Ting Jin misc QH426-470 misc Single-cell genomics misc Single-cell multi-omics integration misc Cell-type gene regulatory network misc Cell-type disease risk genes misc Cross-disease functional genomics misc Schizophrenia misc Medicine misc R misc Genetics scGRNom: a computational pipeline of integrative multi-omics analyses for predicting cell-type disease genes and regulatory networks
authorStr	Ting Jin
ppnlink_with_tag_str_mv	@@773@@(DE-627)594424275
format	electronic Article
delete_txt_mv	keep
author_role	aut aut aut aut aut aut aut
collection	DOAJ
remote_str	true
callnumber-label	QH426-470
illustrated	Not Illustrated
issn	1756994X
topic_title	QH426-470 scGRNom: a computational pipeline of integrative multi-omics analyses for predicting cell-type disease genes and regulatory networks Single-cell genomics Single-cell multi-omics integration Cell-type gene regulatory network Cell-type disease risk genes Cross-disease functional genomics Schizophrenia
topic	misc QH426-470 misc Single-cell genomics misc Single-cell multi-omics integration misc Cell-type gene regulatory network misc Cell-type disease risk genes misc Cross-disease functional genomics misc Schizophrenia misc Medicine misc R misc Genetics
topic_unstemmed	misc QH426-470 misc Single-cell genomics misc Single-cell multi-omics integration misc Cell-type gene regulatory network misc Cell-type disease risk genes misc Cross-disease functional genomics misc Schizophrenia misc Medicine misc R misc Genetics
topic_browse	misc QH426-470 misc Single-cell genomics misc Single-cell multi-omics integration misc Cell-type gene regulatory network misc Cell-type disease risk genes misc Cross-disease functional genomics misc Schizophrenia misc Medicine misc R misc Genetics
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Genome Medicine
hierarchy_parent_id	594424275
hierarchy_top_title	Genome Medicine
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)594424275 (DE-600)2484394-5
title	scGRNom: a computational pipeline of integrative multi-omics analyses for predicting cell-type disease genes and regulatory networks
ctrlnum	(DE-627)DOAJ054999243 (DE-599)DOAJa05e48828d364f5a92653c3fea92879d
title_full	scGRNom: a computational pipeline of integrative multi-omics analyses for predicting cell-type disease genes and regulatory networks
author_sort	Ting Jin
journal	Genome Medicine
journalStr	Genome Medicine
callnumber-first-code	Q
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2021
contenttype_str_mv	txt
container_start_page	15
author_browse	Ting Jin Peter Rehani Mufang Ying Jiawei Huang Shuang Liu Panagiotis Roussos Daifeng Wang
container_volume	13
class	QH426-470
format_se	Elektronische Aufsätze
author-letter	Ting Jin
doi_str_mv	10.1186/s13073-021-00908-9
author2-role	verfasserin
title_sort	scgrnom: a computational pipeline of integrative multi-omics analyses for predicting cell-type disease genes and regulatory networks
callnumber	QH426-470
title_auth	scGRNom: a computational pipeline of integrative multi-omics analyses for predicting cell-type disease genes and regulatory networks
abstract	Abstract Understanding cell-type-specific gene regulatory mechanisms from genetic variants to diseases remains challenging. To address this, we developed a computational pipeline, scGRNom (single-cell Gene Regulatory Network prediction from multi-omics), to predict cell-type disease genes and regulatory networks including transcription factors and regulatory elements. With applications to schizophrenia and Alzheimer’s disease, we predicted disease genes and regulatory networks for excitatory and inhibitory neurons, microglia, and oligodendrocytes. Further enrichment analyses revealed cross-disease and disease-specific functions and pathways at the cell-type level. Our machine learning analysis also found that cell-type disease genes improved clinical phenotype predictions. scGRNom is a general-purpose tool available at https://github.com/daifengwanglab/scGRNom .
abstractGer	Abstract Understanding cell-type-specific gene regulatory mechanisms from genetic variants to diseases remains challenging. To address this, we developed a computational pipeline, scGRNom (single-cell Gene Regulatory Network prediction from multi-omics), to predict cell-type disease genes and regulatory networks including transcription factors and regulatory elements. With applications to schizophrenia and Alzheimer’s disease, we predicted disease genes and regulatory networks for excitatory and inhibitory neurons, microglia, and oligodendrocytes. Further enrichment analyses revealed cross-disease and disease-specific functions and pathways at the cell-type level. Our machine learning analysis also found that cell-type disease genes improved clinical phenotype predictions. scGRNom is a general-purpose tool available at https://github.com/daifengwanglab/scGRNom .
abstract_unstemmed	Abstract Understanding cell-type-specific gene regulatory mechanisms from genetic variants to diseases remains challenging. To address this, we developed a computational pipeline, scGRNom (single-cell Gene Regulatory Network prediction from multi-omics), to predict cell-type disease genes and regulatory networks including transcription factors and regulatory elements. With applications to schizophrenia and Alzheimer’s disease, we predicted disease genes and regulatory networks for excitatory and inhibitory neurons, microglia, and oligodendrocytes. Further enrichment analyses revealed cross-disease and disease-specific functions and pathways at the cell-type level. Our machine learning analysis also found that cell-type disease genes improved clinical phenotype predictions. scGRNom is a general-purpose tool available at https://github.com/daifengwanglab/scGRNom .
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
container_issue	1
title_short	scGRNom: a computational pipeline of integrative multi-omics analyses for predicting cell-type disease genes and regulatory networks
url	https://doi.org/10.1186/s13073-021-00908-9 https://doaj.org/article/a05e48828d364f5a92653c3fea92879d https://doaj.org/toc/1756-994X
remote_bool	true
author2	Peter Rehani Mufang Ying Jiawei Huang Shuang Liu Panagiotis Roussos Daifeng Wang
author2Str	Peter Rehani Mufang Ying Jiawei Huang Shuang Liu Panagiotis Roussos Daifeng Wang
ppnlink	594424275
callnumber-subject	QH - Natural History and Biology
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.1186/s13073-021-00908-9
callnumber-a	QH426-470
up_date	2024-07-04T01:25:05.190Z
_version_	1803609760274055168
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ054999243</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230308185152.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2021 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/s13073-021-00908-9</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ054999243</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJa05e48828d364f5a92653c3fea92879d</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QH426-470</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Ting Jin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">scGRNom: a computational pipeline of integrative multi-omics analyses for predicting cell-type disease genes and regulatory networks</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Understanding cell-type-specific gene regulatory mechanisms from genetic variants to diseases remains challenging. To address this, we developed a computational pipeline, scGRNom (single-cell Gene Regulatory Network prediction from multi-omics), to predict cell-type disease genes and regulatory networks including transcription factors and regulatory elements. With applications to schizophrenia and Alzheimer’s disease, we predicted disease genes and regulatory networks for excitatory and inhibitory neurons, microglia, and oligodendrocytes. Further enrichment analyses revealed cross-disease and disease-specific functions and pathways at the cell-type level. Our machine learning analysis also found that cell-type disease genes improved clinical phenotype predictions. scGRNom is a general-purpose tool available at https://github.com/daifengwanglab/scGRNom .</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Single-cell genomics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Single-cell multi-omics integration</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Cell-type gene regulatory network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Cell-type disease risk genes</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Cross-disease functional genomics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Schizophrenia</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Medicine</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">R</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Genetics</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Peter Rehani</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Mufang Ying</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jiawei Huang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Shuang Liu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Panagiotis Roussos</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Daifeng Wang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Genome Medicine</subfield><subfield code="d">BMC, 2016</subfield><subfield code="g">13(2021), 1, Seite 15</subfield><subfield code="w">(DE-627)594424275</subfield><subfield code="w">(DE-600)2484394-5</subfield><subfield code="x">1756994X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:13</subfield><subfield code="g">year:2021</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:15</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1186/s13073-021-00908-9</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/a05e48828d364f5a92653c3fea92879d</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1186/s13073-021-00908-9</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1756-994X</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">13</subfield><subfield code="j">2021</subfield><subfield code="e">1</subfield><subfield code="h">15</subfield></datafield></record></collection>
score	7.400509

Nicht das Richtige dabei?

Schreiben Sie uns!

scGRNom: a computational pipeline of integrative multi-omics analyses for predicting cell-type disease genes and regulatory networks

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?