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

Research output: Contribution to journalArticlepeer-review

42 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)272-282
Number of pages11
JournalAnalytica Chimica Acta
Volume572
Issue number2
DOIs
Publication statusPublished - 21 Jul 2006
Externally publishedYes

Keywords

  • Classification
  • Linear discriminant analysis
  • Skin sensitization
  • Support vector machine

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