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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/59913


    Title: Feature Selection for Identifying Protein Disordered Regions
    Authors: Hsu, Hui-Huang;Hsieh, Cheng-Wei
    Contributors: 淡江大學資訊工程學系
    Keywords: Disordered protein region;k-Medoids clustering;Feature selection;Proteomics
    Date: 2010-04
    Issue Date: 2011-10-05 22:26:29 (UTC+8)
    Publisher: Singapore: World Scientific Publishing Co. Pte. Ltd.
    Abstract: Determining the structure of a protein is not an easy task, which usually involved a time-consuming and costly process in the web lab. Using computational methods to predict a protein's tertiary structure from its primary structure (the amino acid sequence) is desirable. Disordered regions are segments of a protein that do not have a fixed conformation, which makes the structure prediction harder. Also, these disordered regions are functionally important for a protein. In this research, we would like to identify such regions with a focus on selecting a proper feature set. Three feature selection methods, namely F-score, information gain (IG), and k-medoids clustering, are used for feature selection. The support vector machine (SVM) is then used for classification. The results show that the classification accuracy can be raised with a smaller feature set. The k-medoids clustering feature selection can reduce the number of features from 440 to 150 and improve the accuracy from 84.66 to 86.81% in five-fold cross validation. It also has a more stable performance than F-score and IG.
    Relation: Biomedical Engineering: Applications, Basis and Communications 22(2), pp.119-125
    DOI: 10.4015/S1016237210001839
    Appears in Collections:[Graduate Institute & Department of Computer Science and Information Engineering] Journal Article

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