title: Predicting viral protein subcellular localization with Chou's pseudo amino acid composition and imbalance-weighted multi-label K-nearest neighbor algorithm.
abstract: Machine learning is a kind of reliable technology for automated subcellular localization of viral proteins within a host cell or virus-infected cell. One challenge is that the viral protein samples are not only with multiple location sites, but also class-imbalanced. The imbalanced dataset often decreases the prediction performance. In order to accomplish this challenge, this paper proposes a novel approach named imbalance-weighted multi-label K-nearest neighbor to predict viral protein subcellular location with multiple sites. The experimental results by jackknife test indicate that the presented algorithm achieves a better performance than the existing methods and has great potentials in protein science.
authors.affiliation: School of Control Science and Engineering, Dalian University of Technology, Dalian, Liaoning, China.
authors.forename: Jun-Zhe
authors.initials: JZ
authors.lastname: Cao
authors.forename: Wen-Qi
authors.initials: WQ
authors.lastname: Liu
authors.forename: Hong
authors.initials: H
authors.lastname: Gu
mesh_terms: D000465:Algorithms; D000596:Amino Acids / Q000032:analysis / Q000737:chemistry; D002462:Cell Membrane / Q000737:chemistry / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D054884:Host-Pathogen Interactions; D042541:Intracellular Space / Q000737:chemistry* / Q000378:metabolism*; D015233:Models, Statistical; D014764:Viral Proteins / Q000737:chemistry* / Q000378:metabolism*
chemical_list: D000596:Amino Acids; D014764:Viral Proteins