title: Classification study of skin sensitizers based on support vector machine and linear discriminant analysis.
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.
authors.affiliation: Department of Chemistry, Lanzhou University, Lanzhou 730000, China.
authors.forename: Yueying
authors.initials: Y
authors.lastname: Ren
authors.forename: Huanxiang
authors.initials: H
authors.lastname: Liu
authors.forename: Chunxia
authors.initials: C
authors.lastname: Xue
authors.forename: Xiaojun
authors.initials: X
authors.lastname: Yao
authors.forename: Mancang
authors.initials: M
authors.lastname: Liu
authors.forename: Botao
authors.initials: B
authors.lastname: Fan