MATEC Web of Conferences
Volume 49, 20162016 6th International Conference on Chemistry and Chemical Process (ICCCP 2016)
|Number of page(s)||4|
|Published online||19 April 2016|
Prediction of Bacterial Virulent Proteins with Composition Moment Vector Feature Encoding Method
1 Department of Computer Engineering, Yalova University, Yalova, Turkey
2 Department of Industrial Engineering, Turkish German University, Istanbul, Turkey
a Corresponding author: firstname.lastname@example.org
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.
© Owned by the authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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