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 mole...

• Type: Conference
• Year: 2007
• Where: IMSCCS
• Authors: Guo-Zheng Li, Hao-Hua Meng, Mary Qu Yang, Jack Y. Yang
• Keywords: Asymmetric Bagging, Drug Molecules, Feature Selection, IMSCCS 2007, Theoretical Computer Science