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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/64315

    Title: Improving protein complex classification accuracy using amino acid composition profile
    Authors: 黃建宏;Huang, Chien-Hung;周思瑜;Chou, Szu-Yu;吳家樂;Ng, Ka-Lok
    Contributors: 生物與醫學資訊學系
    Keywords: Protein complex;Protein–protein interaction;Gene Ontology;Sequence alignment;Physicochemical property;Hydrophobic;Hydrophilic;Amino acid composition profile;Machine learning method
    Date: 2013-09
    Issue Date: 2013-10-29 17:36:32 (UTC+8)
    Abstract: Protein complex prediction approaches are based on the assumptions that complexes have dense protein–protein interactions and high functional similarity between their subunits. We investigated those assumptions by studying the subunits' interaction topology, sequence similarity and molecular function for human and yeast protein complexes. Inclusion of amino acids' physicochemical properties can provide better understanding of protein complex properties. Principal component analysis is carried out to determine the major features. Adopting amino acid composition profile information with the SVM classifier serves as an effective post-processing step for complexes classification. Improvement is based on primary sequence information only, which is easy to obtain.
    Relation: COMPUTERS IN BIOLOGY AND MEDICINE, 43(9),Pages 1196–1204.
    Appears in Collections:[生物資訊與醫學工程學系 ] 期刊論文

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