title: Naïve Bayes classifier with feature selection to identify phage virion proteins.
abstract: 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.
authors.affiliation: School of Public Health, Hebei United University, Tangshan 063000, China.
authors.forename: Peng-Mian
authors.initials: PM
authors.lastname: Feng
authors.forename: Hui
authors.initials: H
authors.lastname: Ding
authors.forename: Wei
authors.initials: W
authors.lastname: Chen
authors.forename: Hao
authors.initials: H
authors.lastname: Lin
mesh_terms: D000465:Algorithms; D000596:Amino Acids / Q000032:analysis; D001185:Artificial Intelligence / Q000706:statistics & numerical data; D001499:Bayes Theorem; D019295:Computational Biology; D030562:Databases, Protein / Q000706:statistics & numerical data; D040901:Proteomics / Q000706:statistics & numerical data; D014764:Viral Proteins / Q000737:chemistry* / Q000145:classification*; D014771:Virion / Q000737:chemistry*
chemical_list: D000596:Amino Acids; D014764:Viral Proteins