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    Consensus of sample-balanced classifiers for identifying ligand-binding residue by co-evolutionary physicochemical characteristics of amino acids

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    Type
    Conference Paper
    Authors
    Chen, Peng
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2013
    Permanent link to this record
    http://hdl.handle.net/10754/564671
    
    Metadata
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    Abstract
    Protein-ligand binding is an important mechanism for some proteins to perform their functions, and those binding sites are the residues of proteins that physically bind to ligands. So far, the state-of-the-art methods search for similar, known structures of the query and predict the binding sites based on the solved structures. However, such structural information is not commonly available. In this paper, we propose a sequence-based approach to identify protein-ligand binding residues. Due to the highly imbalanced samples between the ligand-binding sites and non ligand-binding sites, we constructed several balanced data sets, for each of which a random forest (RF)-based classifier was trained. The ensemble of these RF classifiers formed a sequence-based protein-ligand binding site predictor. Experimental results on CASP9 targets demonstrated that our method compared favorably with the state-of-the-art. © Springer-Verlag Berlin Heidelberg 2013.
    Citation
    Chen, P. (2013). Consensus of Sample-Balanced Classifiers for Identifying Ligand-Binding Residue by Co-evolutionary Physicochemical Characteristics of Amino Acids. Emerging Intelligent Computing Technology and Applications, 206–212. doi:10.1007/978-3-642-39678-6_35
    Publisher
    Springer Nature
    Journal
    Emerging Intelligent Computing Technology and Applications
    Conference/Event name
    9th International Conference on Intelligent Computing, ICIC 2013
    ISBN
    9783642396779
    DOI
    10.1007/978-3-642-39678-6_35
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-642-39678-6_35
    Scopus Count
    Collections
    Conference Papers; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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