Predicting viral protein subcellular localization with Chou's pseudo amino acid composition and imbalance-weighted multi-label K-nearest neighbor algorithm

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. © 2012 bentham science publishers.