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Prediction of Bacterial Virulent Proteins with Composition Moment Vector Feature Encoding Method

MATEC Web of Conferences, 2016, Vol.49, p.7001 [Peer Reviewed Journal]

2016. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2261-236X ;ISSN: 2274-7214 ;EISSN: 2261-236X ;DOI: 10.1051/matecconf/20164907001

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  • Title:
    Prediction of Bacterial Virulent Proteins with Composition Moment Vector Feature Encoding Method
  • Author: Gök, Murat ; Herand, Deniz
  • Namieśnik, J. ; Yang, D. ; Kan, C.-W.
  • Subjects: Bacteria ; Classifiers ; Coding ; Composition ; Correlation analysis ; Correlation coefficients ; Proteins ; Sequences ; Virulence
  • Is Part Of: MATEC Web of Conferences, 2016, Vol.49, p.7001
  • Description: Prediction of bacterial virulent proteins is critical for vaccine development and understanding of virulence mechanisms in pathogens. For this purpose, a number of feature encoding methods based on sequences and evolutionary information of a given protein have been proposed and applied with some classifier algorithms so far. In this paper, we performed composition moment vector (CMV), which includes information about both composition and position of amino acid in the protein sequence to predict bacterial virulent proteins. The tests were validated in three different independent datasets. Experimental results show that CMV feature encoding method leads to better classification performance in terms of accuracy, sensitivity, f-measure and the Matthews correlation coefficient (MCC) scores on diverse classifiers.
  • Publisher: Les Ulis: EDP Sciences
  • Language: English
  • Identifier: ISSN: 2261-236X
    ISSN: 2274-7214
    EISSN: 2261-236X
    DOI: 10.1051/matecconf/20164907001
  • Source: EDP Open
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
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