title: Protein function classification via support vector machine approach.
abstract: Support vector machine (SVM) is introduced as a method for the classification of proteins into functionally distinguished classes. Studies are conducted on a number of protein classes including RNA-binding proteins; protein homodimers, proteins responsible for drug absorption, proteins involved in drug distribution and excretion, and drug metabolizing enzymes. Testing accuracy for the classification of these protein classes is found to be in the range of 84-96%. This suggests the usefulness of SVM in the classification of protein functional classes and its potential application in protein function prediction.
authors.affiliation: Department of Applied Physics, Chongqing University, Chongqing 400044, People's Republic of China.
authors.forename: C Z
authors.initials: CZ
authors.lastname: Cai
authors.forename: W L
authors.initials: WL
authors.lastname: Wang
authors.forename: L Z
authors.initials: LZ
authors.lastname: Sun
authors.forename: Y Z
authors.initials: YZ
authors.lastname: Chen
mesh_terms: D000595:Amino Acid Sequence; D019295:Computational Biology / Q000379:methods*; D011506:Proteins / Q000145:classification*
chemical_list: D011506:Proteins