title: Support vector machines for predicting the specificity of GalNAc-transferase.
abstract: Support Vector Machines (SVMs) which is one kind of learning machines, was applied to predict the specificity of GalNAc-transferase. The examination for the self-consistency and the jackknife test of the SVMs method were tested for the training dataset (305 oligopeptides), the correct rate of self-consistency and jackknife test reaches 100% and 84.9%, respectively. Furthermore, the prediction of the independent testing dataset (30 oligopeptides) was tested, the rate reaches 76.67%.
authors.affiliation: Shanghai Research Centre of Biotechnology, Chinese Academy of Sciences, 200233, Shanghai, 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: Kuo Chen
authors.initials: KC
authors.lastname: Chou
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001665:Binding Sites; D008962:Models, Theoretical; D017350:N-Acetylgalactosaminyltransferases / Q000737:chemistry* / Q000378:metabolism*; D012984:Software; D013379:Substrate Specificity; D000097763:Polypeptide N-acetylgalactosaminyltransferase
chemical_list: D017350:N-Acetylgalactosaminyltransferases; D000097763:Polypeptide N-acetylgalactosaminyltransferase