Feature selection of support vector regression for Quantitative Structure-Activity Relationships (QSAR)

被引：0

作者：

Huang, L ^{[1
]}

Lu, HM ^{[1
]}

Dai, Y ^{[1
]}

机构：

[1] Univ Illinois, Dept Bioengn MC063, Chicago, IL 60607 USA

来源：

METMBS'03: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON MATHEMATICS AND ENGINEERING TECHNIQUES IN MEDICINE AND BIOLOGICAL SCIENCES | 2003年

关键词：

QSAR; support vector regression; feature selection; grid search; linear programs;

D O I：

暂无

中图分类号：

R19 [保健组织与事业（卫生事业管理）];

学科分类号：

摘要：

Predicting the biological activity of a compound from its chemical structure is a fundamental problem in drug design. The Support Vector (SV) Machine regression is one of the powerful machine learning methods developed for this purpose in Quantitative Structure-Activity Relationships (QSAR) Analysis. A procedure based on linear programming is proposed for feature selection of SV regression. This new approach demonstrates favorable behavior in comparison with Partial Least Squares (PLS) regression method and a hybrid procedure of combining (1) genetic programming and (2) a neural network for several real compound data.

引用

页码：88 / 93

页数：6

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