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    Semi-Supervised Transductive Hot Spot Predictor Working on Multiple Assumptions

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    Type
    Article
    Authors
    Wang, Jim Jing-Yan
    Almasri, Islam
    Shi, Yuexiang
    Gao, Xin cc
    KAUST Department
    Computational Bioscience Research Center (CBRC)
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2014-05-23
    Permanent link to this record
    http://hdl.handle.net/10754/556612
    
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    Abstract
    Protein-protein interactions are critically dependent on just a few residues (“hot spots”) at the interfaces. Hot spots make a dominant contribution to the binding free energy and if mutated they can disrupt the interaction. As mutagenesis studies require significant experimental efforts, there exists a need for accurate and reliable computational hot spot prediction methods. Compared to the supervised hot spot prediction algorithms, the semi-supervised prediction methods can take into consideration both the labeled and unlabeled residues in the dataset during the prediction procedure. The transductive support vector machine has been utilized for this task and demonstrated a better prediction performance. To the best of our knowledge, however, none of the transductive semi-supervised algorithms takes all the three semisupervised assumptions, i.e., smoothness, cluster and manifold assumptions, together into account during learning. In this paper, we propose a novel semi-supervised method for hot spot residue prediction, by considering all the three semisupervised assumptions using nonlinear models. Our algorithm, IterPropMCS, works in an iterative manner. In each iteration, the algorithm first propagates the labels of the labeled residues to the unlabeled ones, along the shortest path between them on a graph, assuming that they lie on a nonlinear manifold. Then it selects the most confident residues as the labeled ones for the next iteration, according to the cluster and smoothness criteria, which is implemented by a nonlinear density estimator. Experiments on a benchmark dataset, using protein structure-based features, demonstrate that our approach is effective in predicting hot spots and compares favorably to other available methods. The results also show that our method outperforms the state-of-the-art transductive learning methods.
    Citation
    Semi-Supervised Transductive Hot Spot Predictor Working on Multiple Assumptions 2014, 9 (3):258 Current Bioinformatics
    Publisher
    Bentham Science Publishers Ltd.
    Journal
    Current Bioinformatics
    DOI
    10.2174/1574893609999140523124421
    Additional Links
    http://www.eurekaselect.com/openurl/content.php?genre=article&issn=1574-8936&volume=9&issue=3&spage=258
    ae974a485f413a2113503eed53cd6c53
    10.2174/1574893609999140523124421
    Scopus Count
    Collections
    Articles; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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