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
收藏
页码：88 / 93
页数：6
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