Citation: DAI Zhi-Jun, ZHOU Wei, YUAN Zhe-Ming. A Novel Method of Nonlinear Rapid Feature Selection for High Dimensional Data and Its Application in Peptide QSAR Modeling Based on Support Vector Machine[J]. Acta Physico-Chimica Sinica, ;2011, 27(07): 1654-1660. doi: 10.3866/PKU.WHXB20110735 shu

A Novel Method of Nonlinear Rapid Feature Selection for High Dimensional Data and Its Application in Peptide QSAR Modeling Based on Support Vector Machine

DAI Zhi-Jun ^1,2 ,
ZHOU Wei ¹ ,
YUAN Zhe-Ming ^1,2,

1.
1. College of Bio-safety Science and Technology, Hunan Agricultural University, Changsha 410128, P. R. China;
2. Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization, Changsha 410128, P. R. China

Received Date: 24 March 2011
Available Online: 3 June 2011
Fund Project: 湖南省杰出青年科学基金(10JJ1005) (10JJ1005) 高等学校博士点基金(200805370002) (200805370002)

Each amino acid residue of one peptide was characterized directly by 531 physicochemical property parameters. Based on support vector regression (SVR) we developed a new nonlinear rapid feature selection method for high dimensional data, which was applied to a quantitative sequence- activity relationship (QSAR) study of two peptide systems (bitter tasting thresholds and angiotensin converting enzyme inhibitors). In both systems, 10 descriptors with clear meaning were reserved. We established a SVR model for both peptide systems using the reserved descriptors of the peptides. For both models the accuracies of fitting, the leave-one-out cross validation, and the external prediction improved significantly compared with the results reported in literature. To enhance the interpretability of the models, significance tests of the nonlinear regression model, single-factor relative importance, and a single-factor effect analysis were carried out. The new method has broad application prospects for regression forecasting of high dimensional data such as QSAR modeling of peptide or proteins.
- High dimensional feature,
- Feature selection,
- Peptide,
- Quantitative sequence-activity relationship,
- Support vector machine
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