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发布时间：2019-03-09

论文类型：期刊论文

发表时间：2012-11-01

发表刊物：PROTEIN AND PEPTIDE LETTERS

收录刊物：Scopus、PubMed、SCIE

卷号：19

期号：11

页面范围：1163-1169

ISSN号：0929-8665

关键字：Class-imbalance; K-nearest neighbor; multi-label learning; pseudo amino acid composition; subcellular localization

摘要：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.