Generic placeholder image

Protein & Peptide Letters

Editor-in-Chief

ISSN (Print): 0929-8665
ISSN (Online): 1875-5305

Predicting Viral Protein Subcellular Localization with Chou's Pseudo Amino Acid Composition and Imbalance-Weighted Multi-Label K-Nearest Neighbor Algorithm

Author(s): Jun-Zhe Cao, Wen-Qi Liu and Hong Gu

Volume 19 , Issue 11 , 2012

Page: [1163 - 1169] Pages: 7

DOI: 10.2174/092986612803216999

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.

Keywords: Class-imbalance, K-nearest neighbor, multi-label learning, pseudo amino acid composition, subcellular localization


© 2022 Bentham Science Publishers | Privacy Policy