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

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

Volume: 19 Issue: 11

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

Affiliation:

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

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.

Cite this article as:

Cao Jun-Zhe, Liu Wen-Qi and Gu Hong, Predicting Viral Protein Subcellular Localization with Chou's Pseudo Amino Acid Composition and Imbalance-Weighted Multi-Label K-Nearest Neighbor Algorithm, Protein & Peptide Letters 2012; 19 (11) . https://dx.doi.org/10.2174/092986612803216999