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dc.contributor.advisorGao, Xin
dc.contributor.authorFujii, Chisato
dc.date.accessioned2015-04-21T11:37:58Z
dc.date.available2016-04-16T00:00:00Z
dc.date.issued2015-04-16
dc.identifier.doi10.25781/KAUST-1A8VV
dc.identifier.urihttp://hdl.handle.net/10754/550417
dc.description.abstractGene regulatory networks analyze the relationships between genes allowing us to un- derstand the gene regulatory interactions in systems biology. Gene expression data from the microarray experiments is used to obtain the gene regulatory networks. How- ever, the microarray data is discrete, noisy and non-linear which makes learning the networks a challenging problem and existing gene network inference methods do not give consistent results. Current state-of-the-art study uses the average-ranking-based consensus method to combine and average the ranked predictions from individual methods. However each individual method has an equal contribution to the consen- sus prediction. We have developed a linear programming-based consensus approach which uses learned weights from linear programming among individual methods such that the methods have di↵erent weights depending on their performance. Our result reveals that assigning di↵erent weights to individual methods rather than giving them equal weights improves the performance of the consensus. The linear programming- based consensus method is evaluated and it had the best performance on in silico and Saccharomyces cerevisiae networks, and the second best on the Escherichia coli network outperformed by Inferelator Pipeline method which gives inconsistent results across a wide range of microarray data sets.
dc.language.isoen
dc.subjectgene regulatory networks
dc.subjectconsensus
dc.subjectlinear programming
dc.titleLearning Gene Regulatory Networks Computationally from Gene Expression Data Using Weighted Consensus
dc.typeThesis
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.rights.embargodate2016-04-16
thesis.degree.grantorKing Abdullah University of Science and Technology
dc.contributor.committeememberSoloviev, Victor
dc.contributor.committeememberHoehndorf, Robert
thesis.degree.disciplineComputer Science
thesis.degree.nameMaster of Science
dc.rights.accessrightsAt the time of archiving, the student author of this thesis opted to temporarily restrict access to it. The full text of this thesis became available to the public after the expiration of the embargo on 2016-04-16.
refterms.dateFOA2016-04-16T00:00:00Z


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