Learning gene regulatory networks from gene expression data using weighted consensus

Handle URI:
http://hdl.handle.net/10754/622306
Title:
Learning gene regulatory networks from gene expression data using weighted consensus
Authors:
Fujii, Chisato ( 0000-0002-9762-7592 ) ; Kuwahara, Hiroyuki; Yu, Ge; Guo, Lili; Gao, Xin ( 0000-0002-7108-3574 )
Abstract:
An accurate determination of the network structure of gene regulatory systems from high-throughput gene expression data is an essential yet challenging step in studying how the expression of endogenous genes is controlled through a complex interaction of gene products and DNA. While numerous methods have been proposed to infer the structure of gene regulatory networks, none of them seem to work consistently over different data sets with high accuracy. A recent study to compare gene network inference methods showed that an average-ranking-based consensus method consistently performs well under various settings. Here, we propose a linear programming-based consensus method for the inference of gene regulatory networks. Unlike the average-ranking-based one, which treats the contribution of each individual method equally, our new consensus method assigns a weight to each method based on its credibility. As a case study, we applied the proposed consensus method on synthetic and real microarray data sets, and compared its performance to that of the average-ranking-based consensus and individual inference methods. Our results show that our weighted consensus method achieves superior performance over the unweighted one, suggesting that assigning weights to different individual methods rather than giving them equal weights improves the accuracy. © 2016 Elsevier B.V.
KAUST Department:
Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Fujii C, Kuwahara H, Yu G, Guo L, Gao X (2017) Learning gene regulatory networks from gene expression data using weighted consensus. Neurocomputing 220: 23–33. Available: http://dx.doi.org/10.1016/j.neucom.2016.02.087.
Publisher:
Elsevier BV
Journal:
Neurocomputing
KAUST Grant Number:
URF/1/1976-04
Issue Date:
25-Aug-2016
DOI:
10.1016/j.neucom.2016.02.087
Type:
Article
ISSN:
0925-2312
Sponsors:
This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No URF/1/1976-04. This research made use of the resources of the computer clusters at KAUST.
Additional Links:
http://www.sciencedirect.com/science/article/pii/S092523121630916X
Appears in Collections:
Articles; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorFujii, Chisatoen
dc.contributor.authorKuwahara, Hiroyukien
dc.contributor.authorYu, Geen
dc.contributor.authorGuo, Lilien
dc.contributor.authorGao, Xinen
dc.date.accessioned2017-01-02T09:08:25Z-
dc.date.available2017-01-02T09:08:25Z-
dc.date.issued2016-08-25en
dc.identifier.citationFujii C, Kuwahara H, Yu G, Guo L, Gao X (2017) Learning gene regulatory networks from gene expression data using weighted consensus. Neurocomputing 220: 23–33. Available: http://dx.doi.org/10.1016/j.neucom.2016.02.087.en
dc.identifier.issn0925-2312en
dc.identifier.doi10.1016/j.neucom.2016.02.087en
dc.identifier.urihttp://hdl.handle.net/10754/622306-
dc.description.abstractAn accurate determination of the network structure of gene regulatory systems from high-throughput gene expression data is an essential yet challenging step in studying how the expression of endogenous genes is controlled through a complex interaction of gene products and DNA. While numerous methods have been proposed to infer the structure of gene regulatory networks, none of them seem to work consistently over different data sets with high accuracy. A recent study to compare gene network inference methods showed that an average-ranking-based consensus method consistently performs well under various settings. Here, we propose a linear programming-based consensus method for the inference of gene regulatory networks. Unlike the average-ranking-based one, which treats the contribution of each individual method equally, our new consensus method assigns a weight to each method based on its credibility. As a case study, we applied the proposed consensus method on synthetic and real microarray data sets, and compared its performance to that of the average-ranking-based consensus and individual inference methods. Our results show that our weighted consensus method achieves superior performance over the unweighted one, suggesting that assigning weights to different individual methods rather than giving them equal weights improves the accuracy. © 2016 Elsevier B.V.en
dc.description.sponsorshipThis publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No URF/1/1976-04. This research made use of the resources of the computer clusters at KAUST.en
dc.publisherElsevier BVen
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S092523121630916Xen
dc.subjectConsensusen
dc.subjectGene expressionen
dc.subjectGene regulatory networksen
dc.subjectStructure learningen
dc.titleLearning gene regulatory networks from gene expression data using weighted consensusen
dc.typeArticleen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalNeurocomputingen
dc.contributor.institutionKey Laboratory of Space Utilization, Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, 100094, Chinaen
kaust.authorFujii, Chisatoen
kaust.authorKuwahara, Hiroyukien
kaust.authorGao, Xinen
kaust.grant.numberURF/1/1976-04en
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