title: Prediction of apoptosis protein subcellular location using improved hybrid approach and pseudo-amino acid composition.
abstract: Apoptosis proteins are very important for understanding the mechanism of programmed cell death. The apoptosis protein localization can provide valuable information about its molecular function. The prediction of localization of an apoptosis protein is a challenging task. In our previous work we proposed an increment of diversity (ID) method using protein sequence information for this prediction task. In this work, based on the concept of Chou's pseudo-amino acid composition [Chou, K.C., 2001. Prediction of protein cellular attributes using pseudo-amino acid composition. Proteins: Struct. Funct. Genet. (Erratum: Chou, K.C., 2001, vol. 44, 60) 43, 246-255, Chou, K.C., 2005. Using amphiphilic pseudo-amino acid composition to predict enzyme subfamily classes. Bioinformatics 21, 10-19], a different pseudo-amino acid composition by using the hydropathy distribution information is introduced. A novel ID_SVM algorithm combined ID with support vector machine (SVM) is proposed. This method is applied to three data sets (317 apoptosis proteins, 225 apoptosis proteins and 98 apoptosis proteins). The higher predictive success rates than the previous algorithms are obtained by the jackknife tests.
authors.affiliation: Laboratory of Theoretical Biophysics, Department of Physics, College of Sciences and Technology, Inner Mongolia University, Hohhot 010021, China.
authors.forename: Ying-Li
authors.initials: YL
authors.lastname: Chen
authors.forename: Qian-Zhong
authors.initials: QZ
authors.lastname: Li
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D000818:Animals; D017209:Apoptosis / Q000502:physiology; D051017:Apoptosis Regulatory Proteins / Q000032:analysis* / Q000235:genetics; D002477:Cells / Q000378:metabolism*; D030562:Databases, Protein; D016002:Discriminant Analysis; D008954:Models, Biological; D008969:Molecular Sequence Data
chemical_list: D051017:Apoptosis Regulatory Proteins