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    Learning Gene Regulatory Networks Computationally from Gene Expression Data Using Weighted Consensus

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    Name:
    ChisatoFujii MSThesis .pdf
    Size:
    6.334Mb
    Format:
    PDF
    Description:
    Thesis
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    Type
    Thesis
    Authors
    Fujii, Chisato cc
    Advisors
    Gao, Xin cc
    Committee members
    Soloviev, Victor
    Hoehndorf, Robert cc
    Program
    Computer Science
    KAUST Department
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Date
    2015-04-16
    Embargo End Date
    2016-04-16
    Permanent link to this record
    http://hdl.handle.net/10754/550417
    
    Metadata
    Show full item record
    Access Restrictions
    At 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.
    Abstract
    Gene 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.
    Citation
    Fujii, C. (2015). Learning Gene Regulatory Networks Computationally from Gene Expression Data Using Weighted Consensus. KAUST Research Repository. https://doi.org/10.25781/KAUST-1A8VV
    DOI
    10.25781/KAUST-1A8VV
    ae974a485f413a2113503eed53cd6c53
    10.25781/KAUST-1A8VV
    Scopus Count
    Collections
    MS Theses; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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