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  • A Segmentation-Based Method to Extract Structural and Evolutionary Features For Protein Fold Recognition

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    Author(s)
    Dehzangi, Abdollah
    Paliwal, Kuldip
    Lyons, James
    Sharma, Alok
    Sattar, Abdul
    Griffith University Author(s)
    Sattar, Abdul
    Paliwal, Kuldip K.
    Year published
    2014
    Metadata
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    Abstract
    Protein fold recognition (PFR) is considered as an important step towards the protein structure prediction problem. Despite all the efforts that have been made so far, finding an accurate and fast computational approach to solve the PFR still remains a challenging problem for bioinformatics and computational biology. In this study, we propose the concept of segmented-based feature extraction technique to provide local evolutionary information embedded in position specific scoring matrix (PSSM) and structural information embedded in the predicted secondary structure of proteins using SPINE-X. We also employ the concept of ...
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    Protein fold recognition (PFR) is considered as an important step towards the protein structure prediction problem. Despite all the efforts that have been made so far, finding an accurate and fast computational approach to solve the PFR still remains a challenging problem for bioinformatics and computational biology. In this study, we propose the concept of segmented-based feature extraction technique to provide local evolutionary information embedded in position specific scoring matrix (PSSM) and structural information embedded in the predicted secondary structure of proteins using SPINE-X. We also employ the concept of occurrence feature to extract global discriminatory information from PSSM and SPINE-X. By applying a support vector machine (SVM) to our extracted features, we enhance the protein fold prediction accuracy for 7.4 percent over the best results reported in the literature. We also report 73.8 percent prediction accuracy for a data set consisting of proteins with less than 25 percent sequence similarity rates and 80.7 percent prediction accuracy for a data set with proteins belonging to 110 folds with less than 40 percent sequence similarity rates. We also investigate the relation between the number of folds and the number of features being used and show that the number of features should be increased to get better protein fold prediction results when the number of folds is relatively large.
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    Journal Title
    IEEE - ACM Transactions on Computational Biology and Bioinformatics
    Volume
    11
    Issue
    3
    DOI
    https://doi.org/10.1109/TCBB.2013.2296317
    Copyright Statement
    © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subject
    Mathematical sciences
    Biological sciences
    Information and computing sciences
    Publication URI
    http://hdl.handle.net/10072/67305
    Collection
    • Journal articles

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