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Article: Cell fate conversion prediction by group sparse optimization method utilizing single-cell and bulk OMICs data

TitleCell fate conversion prediction by group sparse optimization method utilizing single-cell and bulk OMICs data
Authors
Keywordscell fate conversion
gene regulatory network
group sparse optimization
integrative OMICs
master transcription factor
single-cell genomics
Issue Date2021
Citation
Briefings in Bioinformatics, 2021, v. 22, n. 6, article no. bbab311 How to Cite?
AbstractCell fate conversion by overexpressing defined factors is a powerful tool in regenerative medicine. However, identifying key factors for cell fate conversion requires laborious experimental efforts; thus, many of such conversions have not been achieved yet. Nevertheless, cell fate conversions found in many published studies were incomplete as the expression of important gene sets could not be manipulated thoroughly. Therefore, the identification of master transcription factors for complete and efficient conversion is crucial to render this technology more applicable clinically. In the past decade, systematic analyses on various single-cell and bulk OMICs data have uncovered numerous gene regulatory mechanisms, and made it possible to predict master gene regulators during cell fate conversion. By virtue of the sparse structure of master transcription factors and the group structure of their simultaneous regulatory effects on the cell fate conversion process, this study introduces a novel computational method predicting master transcription factors based on group sparse optimization technique integrating data from multi-OMICs levels, which can be applicable to both single-cell and bulk OMICs data with a high tolerance of data sparsity. When it is compared with current prediction methods by cross-referencing published and validated master transcription factors, it possesses superior performance. In short, this method facilitates fast identification of key regulators, give raise to the possibility of higher successful conversion rate and in the hope of reducing experimental cost.
Persistent Identifierhttp://hdl.handle.net/10722/324512
ISSN
2023 Impact Factor: 6.8
2023 SCImago Journal Rankings: 2.143
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorQin, Jing-
dc.contributor.authorHu, Yaohua-
dc.contributor.authorYao, Jen Chih-
dc.contributor.authorLeung, Ricky Wai Tak-
dc.contributor.authorZhou, Yongqiang-
dc.contributor.authorQin, Yiming-
dc.contributor.authorWang, Junwen-
dc.date.accessioned2023-02-03T07:03:40Z-
dc.date.available2023-02-03T07:03:40Z-
dc.date.issued2021-
dc.identifier.citationBriefings in Bioinformatics, 2021, v. 22, n. 6, article no. bbab311-
dc.identifier.issn1467-5463-
dc.identifier.urihttp://hdl.handle.net/10722/324512-
dc.description.abstractCell fate conversion by overexpressing defined factors is a powerful tool in regenerative medicine. However, identifying key factors for cell fate conversion requires laborious experimental efforts; thus, many of such conversions have not been achieved yet. Nevertheless, cell fate conversions found in many published studies were incomplete as the expression of important gene sets could not be manipulated thoroughly. Therefore, the identification of master transcription factors for complete and efficient conversion is crucial to render this technology more applicable clinically. In the past decade, systematic analyses on various single-cell and bulk OMICs data have uncovered numerous gene regulatory mechanisms, and made it possible to predict master gene regulators during cell fate conversion. By virtue of the sparse structure of master transcription factors and the group structure of their simultaneous regulatory effects on the cell fate conversion process, this study introduces a novel computational method predicting master transcription factors based on group sparse optimization technique integrating data from multi-OMICs levels, which can be applicable to both single-cell and bulk OMICs data with a high tolerance of data sparsity. When it is compared with current prediction methods by cross-referencing published and validated master transcription factors, it possesses superior performance. In short, this method facilitates fast identification of key regulators, give raise to the possibility of higher successful conversion rate and in the hope of reducing experimental cost.-
dc.languageeng-
dc.relation.ispartofBriefings in Bioinformatics-
dc.subjectcell fate conversion-
dc.subjectgene regulatory network-
dc.subjectgroup sparse optimization-
dc.subjectintegrative OMICs-
dc.subjectmaster transcription factor-
dc.subjectsingle-cell genomics-
dc.titleCell fate conversion prediction by group sparse optimization method utilizing single-cell and bulk OMICs data-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1093/bib/bbab311-
dc.identifier.pmid34374760-
dc.identifier.scopuseid_2-s2.0-85121950452-
dc.identifier.volume22-
dc.identifier.issue6-
dc.identifier.spagearticle no. bbab311-
dc.identifier.epagearticle no. bbab311-
dc.identifier.eissn1477-4054-
dc.identifier.isiWOS:000733325700180-

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