title: Prediction of Golgi-resident protein types using general form of Chou's pseudo-amino acid compositions: Approaches with minimal redundancy maximal relevance feature selection.
abstract: Recently, several efforts have been made in predicting Golgi-resident proteins. However, it is still a challenging task to identify the type of a Golgi-resident protein. Precise prediction of the type of a Golgi-resident protein plays a key role in understanding its molecular functions in various biological processes. In this paper, we proposed to use a mutual information based feature selection scheme with the general form Chou's pseudo-amino acid compositions to predict the Golgi-resident protein types. The positional specific physicochemical properties were applied in the Chou's pseudo-amino acid compositions. We achieved 91.24% prediction accuracy in a jackknife test with 49 selected features. It has the best performance among all the present predictors. This result indicates that our computational model can be useful in identifying Golgi-resident protein types.
authors.affiliation: School of Computer Science and Technology, Tianjin University, Tianjin 300354, China. Electronic address: jiaoyasen@tju.edu.cn.
authors.forename: Ya-Sen
authors.initials: YS
authors.lastname: Jiao
authors.affiliation: School of Computer Science and Technology, Tianjin University, Tianjin 300354, China. Electronic address: PufengDu@gmail.com.
authors.forename: Pu-Feng
authors.initials: PF
authors.lastname: Du
mesh_terms: D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry*; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D006056:Golgi Apparatus / Q000378:metabolism*
chemical_list: D000596:Amino Acids
keywords: Feature selection; Golgi-resident proteins; PSPCP; PseAAC; SVM; mRMR