title: Predicting subcellular location of apoptosis proteins with pseudo amino acid composition: approach from amino acid substitution matrix and auto covariance transformation.
abstract: Apoptosis proteins are very important for understanding the mechanism of programmed cell death. Obtaining information on subcellular location of apoptosis proteins is very helpful to understand the apoptosis mechanism. In this paper, based on amino acid substitution matrix and auto covariance transformation, we introduce a new sequence-based model, which not only quantitatively describes the differences between amino acids, but also partially incorporates the sequence-order information. This method is applied to predict the apoptosis proteins' subcellular location of two widely used datasets by the support vector machine classifier. The results obtained by jackknife test are quite promising, indicating that the proposed method might serve as a potential and efficient prediction model for apoptosis protein subcellular location prediction.
authors.affiliation: Department of Mathematics, Shanghai Normal University, 200234, Shanghai, China.
authors.forename: Xiaoqing
authors.initials: X
authors.lastname: Yu
authors.forename: Xiaoqi
authors.initials: X
authors.lastname: Zheng
authors.forename: Taigang
authors.initials: T
authors.lastname: Liu
authors.forename: Yongchao
authors.initials: Y
authors.lastname: Dou
authors.forename: Jun
authors.initials: J
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D019943:Amino Acid Substitution; D000596:Amino Acids / Q000737:chemistry / Q000378:metabolism*; D017209:Apoptosis; D051017:Apoptosis Regulatory Proteins / Q000737:chemistry* / Q000378:metabolism*; D019295:Computational Biology; D060388:Support Vector Machine
chemical_list: D000596:Amino Acids; D051017:Apoptosis Regulatory Proteins