Luan Feng, Zhang Ruisheng, Zhao Chunyan, Yao Xiaojun, Liu Mancang, Hu Zhide, Fan Botao
Department of Chemistry, Lanzhou University, Lanzhou, Gansu 730000, China.
Chem Res Toxicol. 2005 Feb;18(2):198-203. doi: 10.1021/tx049782q.
The support vector machine (SVM), as a novel type of learning machine, was used to develop a classification model of carcinogenic properties of 148 N-nitroso compounds. The seven descriptors calculated solely from the molecular structures of compounds selected by forward stepwise linear discriminant analysis (LDA) were used as inputs of the SVM model. The obtained results confirmed the discriminative capacity of the calculated descriptors. The result of SVM (total accuracy of 95.2%) is better than that of LDA (total accuracy of 89.8%).
支持向量机（SVM）作为一种新型的学习机器，被用于开发148种N-亚硝基化合物致癌特性的分类模型。通过前向逐步线性判别分析（LDA）选择的仅根据化合物分子结构计算得到的七个描述符被用作SVM模型的输入。所得结果证实了所计算描述符的判别能力。SVM的结果（总准确率为95.2%）优于LDA的结果（总准确率为89.8%）。
