title: Support vector machines for prediction of protein domain structural class.
abstract: The support vector machines (SVMs) method was introduced for predicting the structural class of protein domains. The results obtained through the self-consistency test, jack-knife test, and independent dataset test have indicated that the current method and the elegant component-coupled algorithm developed by Chou and co-workers, if effectively complemented with each other, may become a powerful tool for predicting the structural class of protein domains.
authors.affiliation: Shanghai Research Centre of Biotechnology, Chinese Academy of Sciences, Shanghai 200233, China. 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: Kou-Chen
authors.initials: KC
authors.lastname: Chou
mesh_terms: D000465:Algorithms; D000596:Amino Acids / Q000032:analysis; D000818:Animals; D016208:Databases, Factual; D008956:Models, Chemical; D010363:Pattern Recognition, Automated; D017434:Protein Structure, Tertiary
chemical_list: D000596:Amino Acids