Classification study of skin sensitizers based on support vector machine and linear discriminant analysis
Classification study of skin sensitizers based on support vector machine and linear discriminant analysis
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DOI：
10.1016/j.aca.2006.05.027
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发表时间：
2006-07-21
影响因子：
6.2
通讯作者：
Fan, Botao
中科院分区：
文献类型：
--
作者：
Ren, Yueying;Liu, Huanxiang;Fan, Botao
支持向量机（SVM），最近发展起来的机器学习社区，被用来开发一个非线性的二元分类模型的皮肤致敏的131种有机化合物。通过逐步前向判别分析（LDA）从一组不同的分子结构计算的分子描述符，选择了六个描述符。这六个描述符可以反映皮肤致敏的机械相关性，并被用作SVM模型的输入。SVM算法建立的非线性模型的识别效果优于LDA，表明SVM模型在皮肤致敏物识别中具有更高的可靠性。该方法可用于皮肤致敏物的分类，也可推广到其它的QSAR研究中。(c)2006 Elsevier B.V.保留所有权利。
The support vector machine (SVM), recently developed from machine learning community, was used to develop a nonlinear binary classification model of skin sensitization for a diverse set of 131 organic compounds. Six descriptors were selected by stepwise forward discriminant analysis (LDA) from a diverse set of molecular descriptors calculated from molecular structures alone. These six descriptors could reflect the mechanic relevance to skin sensitization and were used as inputs of the SVM model. The nonlinear model developed from SVM algorithm outperformed LDA, which indicated that SVM model was more reliable in the recognition of skin sensitizers. The proposed method is very useful for the classification of skin sensitizers, and can also be extended in other QSAR investigation. (c) 2006 Elsevier B.V. All rights reserved.