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Computational and Mathematical Methods in Medicine
Volume 2013, Article ID 530696, 6 pages
Research Article

Naïve Bayes Classifier with Feature Selection to Identify Phage Virion Proteins

1School of Public Health, Hebei United University, Tangshan 063000, China
2Key Laboratory for Neuroinformation of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
3Department of Physics, School of Sciences, Center for Genomics and Computational Biology, Hebei United University, Tangshan 063000, China

Received 10 March 2013; Revised 16 April 2013; Accepted 28 April 2013

Academic Editor: Yanxin Huang

Copyright © 2013 Peng-Mian Feng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Knowledge about the protein composition of phage virions is a key step to understand the functions of phage virion proteins. However, the experimental method to identify virion proteins is time consuming and expensive. Thus, it is highly desirable to develop novel computational methods for phage virion protein identification. In this study, a Naïve Bayes based method was proposed to predict phage virion proteins using amino acid composition and dipeptide composition. In order to remove redundant information, a novel feature selection technique was employed to single out optimized features. In the jackknife test, the proposed method achieved an accuracy of 79.15% for phage virion and nonvirion proteins classification, which are superior to that of other state-of-the-art classifiers. These results indicate that the proposed method could be as an effective and promising high-throughput method in phage proteomics research.