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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/29104

    Title: An expert system to predict protein thermostability using decision tree
    Authors: Wu,LC;Lee,JX;Huang,HD;Liu,BJ;Horng,JT
    Contributors: 資訊工程研究所
    Date: 2009
    Issue Date: 2010-06-29 20:13:49 (UTC+8)
    Publisher: 中央大學
    Abstract: Protein thermostability information is closely linked to commercial production of many biomaterials. Recent developments have shown that amino acid composition, special sequence patterns and hydrogen bonds, disulfide bonds, salt bridges and so on are of considerable importance to thermostability. In this study, we present a system to integrate these various factors that predict protein thermostability. In this study, the features of proteins in the PGTdb are analyzed. We consider both structure and sequence features and correlation coefficients are incorporated into the feature selection algorithm. Machine learning algorithms are then used to develop identification systems and performances between the different algorithms are compared. In this research, two features, (E + F + M + R)/residue and charged/non-charged, are found to be critical to the thermostability of proteins. Although the sequence and structural models achieve a higher accuracy, sequence-only models provides sufficient accuracy for sequence-only thermostability prediction. (C) 2008 Elsevier Ltd. All rights reserved.
    Appears in Collections:[資訊工程研究所] 期刊論文

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