Hits:

Indexed by:期刊论文

Date of Publication:2012-11-01

Journal:PROTEIN AND PEPTIDE LETTERS

Included Journals:SCIE、PubMed、Scopus

Volume:19

Issue:11

Page Number:1163-1169

ISSN No.:0929-8665

Key Words: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.