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    Computational learning on specificity-determining residue-nucleotide interactions

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    Type
    Article
    Authors
    Wong, Ka-Chun
    Li, Yue
    Peng, Chengbin cc
    Moses, Alan M.
    Zhang, Zhaolei
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2015-11-02
    Permanent link to this record
    http://hdl.handle.net/10754/592815
    
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    Abstract
    The protein–DNA interactions between transcription factors and transcription factor binding sites are essential activities in gene regulation. To decipher the binding codes, it is a long-standing challenge to understand the binding mechanism across different transcription factor DNA binding families. Past computational learning studies usually focus on learning and predicting the DNA binding residues on protein side. Taking into account both sides (protein and DNA), we propose and describe a computational study for learning the specificity-determining residue-nucleotide interactions of different known DNA-binding domain families. The proposed learning models are compared to state-of-the-art models comprehensively, demonstrating its competitive learning performance. In addition, we describe and propose two applications which demonstrate how the learnt models can provide meaningful insights into protein–DNA interactions across different DNA binding families.
    Citation
    Computational learning on specificity-determining residue-nucleotide interactions 2015:gkv1134 Nucleic Acids Research
    Publisher
    Oxford University Press (OUP)
    Journal
    Nucleic Acids Research
    DOI
    10.1093/nar/gkv1134
    PubMed ID
    26527718
    Additional Links
    http://nar.oxfordjournals.org/lookup/doi/10.1093/nar/gkv1134
    ae974a485f413a2113503eed53cd6c53
    10.1093/nar/gkv1134
    Scopus Count
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    Articles; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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