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题名: Quantitative structure-toxicity relationships (QSTRs): A comparative study of various non linear methods. General regression neural network, radial basis function neural network and support vector machine in predicting toxicity of nitro- and cyano- aromatics to Tetrahymena pyriformis
作者: Panaye, A; Fan, BT; Doucet, JP; Yao, XJ(姚小军); Zhang, RS(张瑞生); Liu, MC; Hu, ZD(胡之德)
收录类别: SCIE ; CPCI-S
出版日期: 2006-02-01
会议名称: Conference on Computational Methods in Toxicology and Pharmacology Integrating Internet Resources
会议日期: OCT 29-NOV 01, 2005
会议地点: Shanghai, PEOPLES R CHINA
英文摘要: Prediction of toxicity of 203 nitro- and cyano-aromatic chemicals to Tetrahymena pyriformis was carried out by radial basis function neural network, general regression neural network and support vector machine, in non-linear response surface methodology. Toxicity was predicted from hydrophobicity parameter (log Kow) and maximum superdelocalizability (Amax). Special attention was drawn to prediction ability and robustness of the models. investigated both in a leave-one-out and 10-fold cross validation (CV) processes. The influence that the corresponding changes in the learning sets during these CV processes could have on a common external test set including 41 compounds was also examined. This allowed us to establish the stability of the models. The non linear results slightly outperform (as expected) multilinear relationships (MLR) and also favourably compete with various other non linear approaches recently proposed by Ren (J. Chem. Inf. Comput. Sci., 43 1679 (2003)).
关键词: general regression neural network ; radial basis function neural network ; support vector machine ; tetrahymena ; toxicity ; aromatics
作者部门: Univ Paris 07, ITODYS, CNRS, UMR 7086, F-75005 Paris, France ; Lanzhou Univ, Dept Chem, Lanzhou 730000, Peoples R China
通讯作者: Doucet, JP (reprint author), Univ Paris 07, ITODYS, CNRS, UMR 7086, 1 Rue Guy Brosse, F-75005 Paris, France.
学科分类: Chemistry; Computer Science; Environmental Sciences & Ecology; Mathematical & Computational Biology; Toxicology
会议录: SAR AND QSAR IN ENVIRONMENTAL RESEARCH
期号: 1
页码: 75-91
出版者: TAYLOR & FRANCIS
出版地: ABINGDON
语种: 英语
DOI: 10.1080/10659360600562079
ISSN号: 1062-936X
WOS记录号: WOS:000236110000006
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内容类型: 会议论文
URI标识: http://ir.lzu.edu.cn/handle/262010/113160
Appears in Collections:化学化工学院_会议论文

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Panaye, A,Fan, BT,Doucet, JP,et al. Quantitative structure-toxicity relationships (QSTRs): A comparative study of various non linear methods. General regression neural network, radial basis function neural network and support vector machine in predicting toxicity of nitro- and cyano- aromatics to Tetrahymena pyriformis[C]. 见:Conference on Computational Methods in Toxicology and Pharmacology Integrating Internet Resources. Shanghai, PEOPLES R CHINA. OCT 29-NOV 01, 2005.
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