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Conference Paper: Inferring Gene Regulatory Networks from Integrative Omics Data via LASSO-type regularization methods

TitleInferring Gene Regulatory Networks from Integrative Omics Data via LASSO-type regularization methods
Authors
Issue Date2014
PublisherAcademic Press. The Journal's web site is located at http://www.elsevier.com/locate/ymeth
Citation
Methods, v. 67 n. 3, p. 294-303 How to Cite?
AbstractInferring gene regulatory networks from gene expression data at whole genome level is still an arduous challenge, especially in higher organisms where the number of genes is large but the number of experimental samples is small. It is reported that the accuracy of current methods at genome scale significantly drops from Escherichia coli to Saccharomyces cerevisiae due to the increase in number of genes. This limits the applicability of current methods to more complex genomes, like human and mouse. Least absolute shrinkage and selection operator (LASSO) is widely used for gene regulatory network inference from gene expression profiles. However, the accuracy of LASSO on large genomes is not satisfactory. In this study, we apply two extended models of LASSO, L0 and L1/2 regularization models to infer gene regulatory network from both high-throughput gene expression data and transcription factor binding data in mouse embryonic stem cells (mESCs). We find that both the L0 and L1/2 regularization models significantly outperform LASSO in network inference. Incorporating interactions between transcription factors and their targets remarkably improved the prediction accuracy. Current study demonstrates the efficiency and applicability of these two models for gene regulatory network inference from integrative omics data in large genomes. The applications of the two models will facilitate biologists to study the gene regulation of higher model organisms in a genome-wide scale.
Persistent Identifierhttp://hdl.handle.net/10722/189748
ISSN
2015 Impact Factor: 3.503
2015 SCImago Journal Rankings: 2.377
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorQIN, J-
dc.contributor.authorHU, YH-
dc.contributor.authorXU, F-
dc.contributor.authorYalamanchili, HK-
dc.contributor.authorWang, JJ-
dc.date.accessioned2013-09-17T14:56:02Z-
dc.date.available2013-09-17T14:56:02Z-
dc.date.issued2014-
dc.identifier.citationMethods, v. 67 n. 3, p. 294-303-
dc.identifier.issn1046-2023-
dc.identifier.urihttp://hdl.handle.net/10722/189748-
dc.description.abstractInferring gene regulatory networks from gene expression data at whole genome level is still an arduous challenge, especially in higher organisms where the number of genes is large but the number of experimental samples is small. It is reported that the accuracy of current methods at genome scale significantly drops from Escherichia coli to Saccharomyces cerevisiae due to the increase in number of genes. This limits the applicability of current methods to more complex genomes, like human and mouse. Least absolute shrinkage and selection operator (LASSO) is widely used for gene regulatory network inference from gene expression profiles. However, the accuracy of LASSO on large genomes is not satisfactory. In this study, we apply two extended models of LASSO, L0 and L1/2 regularization models to infer gene regulatory network from both high-throughput gene expression data and transcription factor binding data in mouse embryonic stem cells (mESCs). We find that both the L0 and L1/2 regularization models significantly outperform LASSO in network inference. Incorporating interactions between transcription factors and their targets remarkably improved the prediction accuracy. Current study demonstrates the efficiency and applicability of these two models for gene regulatory network inference from integrative omics data in large genomes. The applications of the two models will facilitate biologists to study the gene regulation of higher model organisms in a genome-wide scale.-
dc.languageeng-
dc.publisherAcademic Press. The Journal's web site is located at http://www.elsevier.com/locate/ymeth-
dc.relation.ispartofMethods-
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in [Journal title]. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in PUBLICATION, [VOL#, ISSUE#, (DATE)] DOI#-
dc.subject.meshChIP-seq/chip-
dc.subject.meshGene regulatory networks-
dc.subject.meshIntegrative omics data-
dc.subject.meshLASSO-type regularization methods-
dc.titleInferring Gene Regulatory Networks from Integrative Omics Data via LASSO-type regularization methods-
dc.typeConference_Paper-
dc.identifier.emailWang, JJ: junwen@hku.hk-
dc.identifier.authorityWang, JJ=rp00280-
dc.identifier.doi10.1016/j.ymeth.2014.03.006-
dc.identifier.pmid24650566-
dc.identifier.scopuseid_2-s2.0-84901463273-
dc.identifier.hkuros224191-
dc.identifier.hkuros228540-
dc.identifier.volume67-
dc.identifier.issue3-
dc.identifier.spage294-
dc.identifier.epage303-
dc.identifier.isiWOS:000336883500005-
dc.publisher.placeSan Diego-

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