CAJ | 학술논문

从蛋白质一级序列抽取蛋白质结构域信息建立结构域特征向量，使用结构域特征向量训练基于高斯径向基核的支撑向量机，用5折交叉验证对支撑向量机参数网格寻优，然后用最优化参数训练得到蛋白质相互作用预测器。结果表明：预测器的交叉验证ROC曲线下面积达到了85．49％，可以有效预测拟南芥相关的蛋白质相互作用。
종단백질일급서렬추취단백질결구역신식건립결구역특정향량，사용결구역특정향량훈련기우고사경향기핵적지탱향량궤，용5절교차험증대지탱향량궤삼수망격심우，연후용최우화삼수훈련득도단백질상호작용예측기。결과표명：예측기적교차험증ROC곡선하면적체도료85．49％，가이유효예측의남개상관적단백질상호작용。
We extracted the protein domain information from the first level structure , and constructed the feature vectors by the domain information .A support vector machine ( SVM) was trained by the feature vectors , and the five-fold cross-validation was used to conduct the grid optimization for the SVM parameters to get the protein-protein interaction predictor trained by the optimum parameters .The area under ROC of cross-validation reached 85.49 with good effectiveness of the predictor .