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    ProClusEnsem: Predicting membrane protein types by fusing different modes of pseudo amino acid composition

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    Type
    Article
    Authors
    Wang, Jim Jing-Yan
    Li, Yongping
    Wang, Quanquan
    You, Xinge
    Man, Jiaju
    Wang, Chao
    Gao, Xin cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    Computational Bioscience Research Center (CBRC)
    Structural and Functional Bioinformatics Group
    Date
    2012-05
    Permanent link to this record
    http://hdl.handle.net/10754/562173
    
    Metadata
    Show full item record
    Abstract
    Knowing the type of an uncharacterized membrane protein often provides a useful clue in both basic research and drug discovery. With the explosion of protein sequences generated in the post genomic era, determination of membrane protein types by experimental methods is expensive and time consuming. It therefore becomes important to develop an automated method to find the possible types of membrane proteins. In view of this, various computational membrane protein prediction methods have been proposed. They extract protein feature vectors, such as PseAAC (pseudo amino acid composition) and PsePSSM (pseudo position-specific scoring matrix) for representation of protein sequence, and then learn a distance metric for the KNN (K nearest neighbor) or NN (nearest neighbor) classifier to predicate the final type. Most of the metrics are learned using linear dimensionality reduction algorithms like Principle Components Analysis (PCA) and Linear Discriminant Analysis (LDA). Such metrics are common to all the proteins in the dataset. In fact, they assume that the proteins lie on a uniform distribution, which can be captured by the linear dimensionality reduction algorithm. We doubt this assumption, and learn local metrics which are optimized for local subset of the whole proteins. The learning procedure is iterated with the protein clustering. Then a novel ensemble distance metric is given by combining the local metrics through Tikhonov regularization. The experimental results on a benchmark dataset demonstrate the feasibility and effectiveness of the proposed algorithm named ProClusEnsem. © 2012 Elsevier Ltd.
    Citation
    Wang, J., Li, Y., Wang, Q., You, X., Man, J., Wang, C., & Gao, X. (2012). ProClusEnsem: Predicting membrane protein types by fusing different modes of pseudo amino acid composition. Computers in Biology and Medicine, 42(5), 564–574. doi:10.1016/j.compbiomed.2012.01.012
    Sponsors
    The study was supported by grants from Shanghai Key Laboratory of Intelligent Information Processing, China (Grant No. IIPL-2011-003), Key Laboratory of High Performance Computing and Stochastic Information Processing, Ministry of Education of China (Grant No. HS201107), National Grand Fundamental Research (973) Program of China (Grant Nos. 2010CB834303 and 2011CB911102), National Natural Science Foundation of China (Grant No. 60973154), Hubei Provincial Science Foundation, China (Grant Nos. 2010CDA006 and 2010CD06601), and a grant from King Abdullah University of Science and Technology.
    Publisher
    Elsevier BV
    Journal
    Computers in Biology and Medicine
    DOI
    10.1016/j.compbiomed.2012.01.012
    PubMed ID
    22386149
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.compbiomed.2012.01.012
    Scopus Count
    Collections
    Articles; Structural and Functional Bioinformatics Group; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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