title: Predicting Golgi-resident protein types using pseudo amino acid compositions: Approaches with positional specific physicochemical properties.
abstract: Knowing the type of a Golgi-resident protein is an important step in understanding its molecular functions as well as its role in biological processes. In this paper, we developed a novel computational method to predict Golgi-resident protein types using positional specific physicochemical properties and analysis of variance based feature selection methods. Our method achieved 86.9% prediction accuracy in leave-one-out cross-validations with only 59 features. Our method has the potential to be applied in predicting a wide range of protein attributes.
authors.affiliation: School of Computer Science and Technology, Tianjin University, Tianjin 300072, China.
authors.forename: Ya-Sen
authors.initials: YS
authors.lastname: Jiao
authors.affiliation: School of Computer Science and Technology, Tianjin University, Tianjin 300072, China. Electronic address: pufengdu@gmail.com.
authors.forename: Pu-Feng
authors.initials: PF
authors.lastname: Du
mesh_terms: D000818:Animals; D006056:Golgi Apparatus / Q000235:genetics / Q000378:metabolism; D006801:Humans; D011506:Proteins / Q000235:genetics / Q000378:metabolism; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D011506:Proteins
keywords: ANOVA; Golgi apparatus; PSPCP; PseAAC; SVM