title: Prediction of protein structural classes by support vector machines.
abstract: In this paper, we apply a new machine learning method which is called support vector machine to approach the prediction of protein structural class. The support vector machine method is performed based on the database derived from SCOP which is based upon domains of known structure and the evolutionary relationships and the principles that govern their 3D structure. As a result, high rates of both self-consistency and jackknife test are obtained. This indicates that the structural class of a protein inconsiderably correlated with its amino and composition, and the support vector machine can be referred as a powerful computational tool for predicting the structural classes of proteins.
authors.affiliation: Shanghai Research Centre of Biotechnology, Chinese Academy of Sciences. y.cai@umist.ac.uk
authors.forename: Yu-Dong
authors.initials: YD
authors.lastname: Cai
authors.forename: Xiao-Jun
authors.initials: XJ
authors.lastname: Liu
authors.forename: Xue-biao
authors.initials: XB
authors.lastname: Xu
authors.forename: Kuo-Chen
authors.initials: KC
authors.lastname: Chou
mesh_terms: D001185:Artificial Intelligence; D030562:Databases, Protein; D015233:Models, Statistical; D015394:Molecular Structure; D016571:Neural Networks, Computer; D011506:Proteins / Q000737:chemistry*
chemical_list: D011506:Proteins