Classification study of skin sensitizers based on support vector machine and linear discriminant analysis

Yueying Ren, Huanxiang Liu, Chunxia Xue, Xiaojun Yao, Mancang Liu, Botao Fan

研究成果: Article同行評審

42 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)272-282
頁數11
期刊Analytica Chimica Acta
572
發行號2
DOIs
出版狀態Published - 21 7月 2006
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