RESUMO
A quantitative structure-property relationship (QSPR) model in terms of amino acid composition and the activity of Bacillus thuringiensis insecticidal crystal proteins was established. Support vector machine (SVM) is a novel general machine-learning tool based on the structural risk minimization principle that exhibits good generalization when fault samples are few; it is especially suitable for classification, forecasting, and estimation in cases where small amounts of samples are involved such as fault diagnosis; however, some parameters of SVM are selected based on the experience of the operator, which has led to decreased efficiency of SVM in practical application. The uniform design (UD) method was applied to optimize the running parameters of SVM. It was found that the average accuracy rate approached 73% when the penalty factor was 0.01, the epsilon 0.2, the gamma 0.05, and the range 0.5. The results indicated that UD might be used an effective method to optimize the parameters of SVM and SVM and could be used as an alternative powerful modeling tool for QSPR studies of the activity of Bacillus thuringiensis (Bt) insecticidal crystal proteins. Therefore, a novel method for predicting the insecticidal activity of Bt insecticidal crystal proteins was proposed by the authors of this study.
Assuntos
Animais , Algoritmos , Aminoácidos , Genética , Inteligência Artificial , Proteínas de Bactérias , Classificação , Genética , Toxicidade , Sobrevivência Celular , Besouros , Dípteros , Endotoxinas , Classificação , Genética , Toxicidade , Proteínas Hemolisinas , Classificação , Genética , Toxicidade , Controle de Insetos , Métodos , Inseticidas , Toxicidade , Lepidópteros , Modelos Biológicos , Reprodutibilidade dos Testes , Testes de Toxicidade , MétodosRESUMO
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.