title: [Protein structural class prediction with binary tree-based support vector machines].
abstract: A new mutil-classification method based on binary tree SVM (BT-SVM) is presented to predict protein structural class. The protein sequence, which is represented by 26-D vector, is used as input vector. BT-SVM method resolves unclassifiable regions for multiclass problems which can not be solved by SVM. Self-consistency and cross validation test are used to verify the performance of the proposal method on two benchmark datasets. Satisfactory test results demonstrate that the new method is promising. The Jackknife results of the new method are compared with the existing results on the same datasets. The results of the new method are almost the same as the ones of the best exiting method. It illuminates that the new method has good prediction performance and it will become a useful tool in protein structure class prediction.
authors.affiliation: College of Information Sciences and Technology, Donghua University, Shanghai 201620, China.
authors.forename: Tongliang
authors.initials: T
authors.lastname: Zhang
authors.forename: Yongsheng
authors.initials: Y
authors.lastname: Ding
mesh_terms: D019295:Computational Biology / Q000379:methods*; D006801:Humans; D011237:Predictive Value of Tests; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D020539:Sequence Analysis, Protein / Q000379:methods
chemical_list: D011506:Proteins