title: Protein functional class prediction using global encoding of amino acid sequence.
abstract: A key goal of the post-genomic era is to determine protein functions. In this paper, we proposed a global encoding method of protein sequence (GE) to descript global information of amino acid sequence, and then assign protein functional class using machine learning methods nearest neighbor algorithm (NNA). We predicted the function of 1818 Saccharomyces cerevisiae proteins which was used in Vazquez's global optimization method (GOM) except eight proteins which cannot get from the database now or whose sequence length is too short. Using our approach, the computed accuracy is better than Vazquez's global optimization method (GOM) in some cases. The experiment results show that our new method is efficient to predict functional class of unknown proteins.
authors.affiliation: School of Computer and Communication, Hunan University, Changsha Hunan 410082, China.
authors.forename: Xi
authors.initials: X
authors.lastname: Li
authors.forename: Bo
authors.initials: B
authors.lastname: Liao
authors.forename: Yu
authors.initials: Y
authors.lastname: Shu
authors.forename: Qingguang
authors.initials: Q
authors.lastname: Zeng
authors.forename: Jiawei
authors.initials: J
authors.lastname: Luo
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D002627:Chemistry, Physical; D029701:Saccharomyces cerevisiae Proteins / Q000737:chemistry / Q000235:genetics / Q000502:physiology*
chemical_list: D029701:Saccharomyces cerevisiae Proteins