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  • Proposing a highly accurate protein structural class predictor using segmentation-based features

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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
    Background Prediction of the structural classes of proteins can provide important information about their functionalities as well as their major tertiary structures. It is also considered as an important step towards protein structure prediction problem. Despite all the efforts have been made so far, finding a fast and accurate computational approach to solve protein structural class prediction problem still remains a challenging problem in bioinformatics and computational biology. Results In this study we propose segmented distribution and segmented auto covariance feature extraction methods to capture local and global ...
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    Background Prediction of the structural classes of proteins can provide important information about their functionalities as well as their major tertiary structures. It is also considered as an important step towards protein structure prediction problem. Despite all the efforts have been made so far, finding a fast and accurate computational approach to solve protein structural class prediction problem still remains a challenging problem in bioinformatics and computational biology. Results In this study we propose segmented distribution and segmented auto covariance feature extraction methods to capture local and global discriminatory information from evolutionary profiles and predicted secondary structure of the proteins. By applying SVM to our extracted features, for the first time we enhance the protein structural class prediction accuracy to over 90% and 85% for two popular low-homology benchmarks that have been widely used in the literature. We report 92.2% and 86.3% prediction accuracies for 25PDB and 1189 benchmarks which are respectively up to 7.9% and 2.8% better than previously reported results for these two benchmarks. Conclusion By proposing segmented distribution and segmented auto covariance feature extraction methods to capture local and global discriminatory information from evolutionary profiles and predicted secondary structure of the proteins, we are able to enhance the protein structural class prediction performance significantly.
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    Journal Title
    BMC Genomics
    Volume
    15
    Issue
    Suppl1
    DOI
    https://doi.org/10.1186/1471-2164-15-S1-S2
    Copyright Statement
    © 2014 Dehzangi et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
    Subject
    Biological sciences
    Information and computing sciences
    Biomedical and clinical sciences
    Publication URI
    http://hdl.handle.net/10072/66874
    Collection
    • Journal articles

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