title: Evaluation of mutual information, genetic algorithm and SVR for feature selection in QSAR regression.
abstract: Feature selection has become increasingly important for quantitative structure-activity relationship (QSAR) studies. In the present article, we evaluate three state-of-the-art feature selection algorithms, namely mutual information (MI), genetic algorithm (GA), and support vector machine regression (SVR)-based recursive feature elimination (SVR-RFE), in the reduction of high dimensional feature space for QSAR regression. We used SVR to evaluate the performance of these feature selection algorithms. In addition, we present a simple but very efficient iterative strategy for optimizing parameters for SVM-RFE algorithm. All three algorithms can effectively reduce the number of features and often achieve improved performance.
authors.affiliation: Applied Bioinformatics Laboratory, the University of Kansas, Lawrence, 66047, USA. jwfang@ku.edu
authors.forename: Jianwen
authors.initials: J
authors.lastname: Fang
authors.forename: David
authors.initials: D
authors.lastname: Tai
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D017076:Computer-Aided Design; D015195:Drug Design; D015394:Molecular Structure; D004364:Pharmaceutical Preparations / Q000737:chemistry*; D021281:Quantitative Structure-Activity Relationship; D012044:Regression Analysis; D013678:Technology, Pharmaceutical / Q000379:methods*
chemical_list: D004364:Pharmaceutical Preparations