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    Evolving Transcription Factor Binding Site Models From Protein Binding Microarray Data

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    07396941.pdf
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    Type
    Article
    Authors
    Wong, Ka-Chun
    Peng, Chengbin cc
    Li, Yue
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2016-02-02
    Online Publication Date
    2016-02-02
    Print Publication Date
    2016
    Permanent link to this record
    http://hdl.handle.net/10754/597027
    
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    Abstract
    Protein binding microarray (PBM) is a high-throughput platform that can measure the DNA binding preference of a protein in a comprehensive and unbiased manner. In this paper, we describe the PBM motif model building problem. We apply several evolutionary computation methods and compare their performance with the interior point method, demonstrating their performance advantages. In addition, given the PBM domain knowledge, we propose and describe a novel method called kmerGA which makes domain-specific assumptions to exploit PBM data properties to build more accurate models than the other models built. The effectiveness and robustness of kmerGA is supported by comprehensive performance benchmarking on more than 200 datasets, time complexity analysis, convergence analysis, parameter analysis, and case studies. To demonstrate its utility further, kmerGA is applied to two real world applications: 1) PBM rotation testing and 2) ChIP-Seq peak sequence prediction. The results support the biological relevance of the models learned by kmerGA, and thus its real world applicability.
    Citation
    Evolving Transcription Factor Binding Site Models From Protein Binding Microarray Data 2016:1 IEEE Transactions on Cybernetics
    Sponsors
    This work was supported in part by the City University of Hong Kong under Project 7200444/CS, and in part by the Amazon Web Service Research Grant.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Transactions on Cybernetics
    DOI
    10.1109/TCYB.2016.2519380
    Additional Links
    http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7396941
    ae974a485f413a2113503eed53cd6c53
    10.1109/TCYB.2016.2519380
    Scopus Count
    Collections
    Articles; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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