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学科主题: 计算机化学与化学信息学
题名: Prediction of mutagenic toxicity by combination of recursive partitioning and support vector machines
其他题名: 递归分类与支持向量机结合预测致突变毒性
作者: Liao Q(廖泉) ; Yao JH(姚建华) ; Yuan SG(袁身刚)
通讯作者: 姚建华
刊名: Mol. Divers.
发表日期: 2007-01-01
卷: 11, 期:2, 页:59-72
收录类别: SCI
部门归属: 中国科学院上海有机化学研究所
英文摘要: 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.
语种: 英语
相关网址: 查看原文
WOS记录号: WOS:000248837000001
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内容类型: 期刊论文
URI标识: http://ir.sioc.ac.cn/handle/331003/20619
Appears in Collections:计算机化学与化学信息学研究室_期刊论文

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Recommended Citation:
Liao Q,Yao JH,Yuan SG. Prediction of mutagenic toxicity by combination of recursive partitioning and support vector machines[J]. Mol. Divers.,2007,11(2):59-72.
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