Prediction of mutagenic toxicity by combination of Recursive Partitioning and Support Vector Machines
Prediction of mutagenic toxicity by combination
of Recursive Partitioning and Support Vector Machines
复制标题
递归划分和支持向量机相结合的诱变毒性预测
作者：
Q. Liao;J. Yao;S. Yuan
由于毒性的测量通常是耗时且昂贵的，因此毒性预测的研究是非常重要和必要的。本文采用递归划分方法进行描述子的选择。分别采用RP和支持向量机（SVM）建立了结构-毒性关系模型、RP模型和SVM模型。两种模型的性能是不同的。RP模型对MDL数据库中致突变化合物、CMC数据库中致突变化合物和农药数据库中致突变化合物的预测准确率分别为80.2%、83.4%和84.9%。SVM模型的预测准确率分别为81.4%、87.0%和87.3%。
The study of prediction of toxicity is very important and necessary because measurement of toxicity is typically time-consuming and expensive. In this paper, Recursive Partitioning (RP) method was used to select descriptors. RP and Support Vector Machines (SVM) were used to construct structure–toxicity relationship models, RP model and SVM model, respectively. The performances of the two models are different. The prediction accuracies of the RP model are 80.2% for mutagenic compounds in MDL’s toxicity database, 83.4% for compounds in CMC and 84.9% for agrochemicals in in-house database respectively. Those of SVM model are 81.4%, 87.0% and 87.3% respectively.
DOI：
10.1016/0165-1161(92)90008-a
发表时间：
1992-08
期刊：
Mutation research
影响因子：
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作者：
Gilles Klopman;Herbert S. Rosenkranz
通讯作者：
Gilles Klopman;Herbert S. Rosenkranz