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

[ Vol. 19 , Issue. 11 ]

Author(s):

Jun-Zhe Cao, Wen-Qi Liu and Hong Gu   Pages 1163 - 1169 ( 7 )

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

Affiliation:

School of Control Science and Engineering, Dalian University of Technology, No.2 Ling-gong Road, Dalian, Liaoning, China.



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