苏云金杆菌杀虫晶体蛋白活性预测的支持向量机模型

A Prediction Model for the Activity of Insecticidal Crystal Proteins from Bacillus thuringiensis Based on Support Vector Machine

摘要:

藉均匀设计(UD)方法,构建了苏云金杆菌（Bt）杀虫晶体蛋白氨基酸组成特征与其杀虫活性之间关系的支持向量机（SVM）模型。当惩罚系数为0.01、epsilon值为0.2、gamma值为0.05、域值为0.5时，该模型对Bt杀虫晶体蛋白杀虫活性的预测平均准确率达73％。

Abstract:

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.