Asymmetric bagging and feature selection for activities prediction of drug molecules.
BACKGROUND
Activities of drug molecules can be predicted by QSAR (quantitative structure activity relationship) models, which overcomes the disadvantages of high cost and long cycle by employing the traditional experimental method. With the fact that the number of drug molecules with positive activity is rather fewer than that of negatives, it is important to predict molecular activities considering such an unbalanced situation.


RESULTS
Here, asymmetric bagging and feature selection are introduced into the problem and asymmetric bagging of support vector machines (asBagging) is proposed on predicting drug activities to treat the unbalanced problem. At the same time, the features extracted from the structures of drug molecules affect prediction accuracy of QSAR models. Therefore, a novel algorithm named PRIFEAB is proposed, which applies an embedded feature selection method to remove redundant and irrelevant features for asBagging. Numerical experimental results on a data set of molecular activities show that asBagging improve the AUC and sensitivity values of molecular activities and PRIFEAB with feature selection further helps to improve the prediction ability.


CONCLUSIONS
Asymmetric bagging can help to improve prediction accuracy of activities of drug molecules, which can be furthermore improved by performing feature selection to select relevant features from the drug molecules data sets.
Li
Guo-Zheng
GZ
Institute of Systems Biology, Shanghai University, Shanghai 200444, China. gzli@shu.edu.cn
Meng
Hao-Hua
HH
Lu
Wen-Cong
WC
Yang
Jack Y
JY
Yang
Mary Qu
MQ
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D008956:Models, Chemical; D010363:Pattern Recognition, Automated / Q000379:methods*; D004364:Pharmaceutical Preparations / Q000737:chemistry*; D021281:Quantitative Structure-Activity Relationship
D004364:Pharmaceutical Preparations