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🏛️國立陽明交通大學機構典藏

Author: search.filters.author.Huang, Hsien-DaSubject: search.filters.subject.Machine learning

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    Prediction of small non-coding RNA in bacterial genomes using support vector machines
    (2010-08-01)
    Chang, Tzu-Hao
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    Wu, Li-Ching
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    Lin, Jun-Hong
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    Huang, Hsien-Da
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    Liu, Baw-Jhiune
    Small non-coding RNA genes have been shown to play important regulatory roles in a variety of cellular processes, but prediction of non-coding RNA genes is a great challenge, using either an experimental or a computational approach, due to the characteristics of sRNAs, which are that sRNAs are small in size, are not translated into proteins and show variable stability. Most known sRNAs have been identified in Escherichia coli and have been shown to be conserved in closely related organisms. We have developed an integrative approach that searches highly conserved intergenic regions among related bacterial genomes for combinations of characteristics that have been extracted from known E. coli sRNA genes. Support vector machines (SVM) were then used with these characteristics to predict novel sRNA genes. (c) 2010 Elsevier Ltd. All rights reserved.
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    An expert system to predict protein thermostability using decision tree
    (2009-07-01)
    Wu, Li-Cheng
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    Lee, Jian-Xin
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    Huang, Hsien-Da
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    Liu, Baw-Juine
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    Horng, Jorng-Tzong
    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.