title: [A uniform design based PCA-SVM model for predicting optimum pH in chitinase].
abstract: The principal component analysis (PCA) was applied to the data processing in training sets, the new principal components were then used as input data for support vector machine model. A prediction model for optimum pH of chitinase was established based on uniform design. When The regularized constant C, epsilon and Gamma were 10, 0.7 and 0.5 respectively, the calculated pHs fitted the reported optimum pHs of chitinase very well and the MAPEs (Mean Absolute Percent Error) was 3.76%. At the same time, the predicted pHs fitted the reported optimum pHs well and the MAE (Mean Absolute Error) was 0.42 pH unit. It was superior in fittings and predictions compared to the model based on back propagation (BP) neural network.
authors.affiliation: Department of Bioengineering & Biotechnology, Huaqiao University, Key Laboratory of Industrial Biotechnology of Fujian Province University, Quanzhou 362021, China. lyhxm@hqu.edu.cn
authors.forename: Yi
authors.initials: Y
authors.lastname: Lin
authors.forename: Fu-Ying
authors.initials: FY
authors.lastname: Cai
authors.forename: Yu-Xi
authors.initials: YX
authors.lastname: Yuan
authors.forename: Guang-Ya
authors.initials: GY
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D000818:Animals; D002688:Chitinases / Q000737:chemistry / Q000378:metabolism*; D006801:Humans; D006863:Hydrogen-Ion Concentration; D008954:Models, Biological; D015233:Models, Statistical; D016571:Neural Networks, Computer; D025341:Principal Component Analysis
chemical_list: D002688:Chitinases