Asymmetric bagging and feature selection for activities prediction of drug molecules
Open Access
- 28 May 2008
- journal article
- Published by Springer Nature in BMC Bioinformatics
- Vol. 9 (S6) , S7
Abstract
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.Keywords
This publication has 28 references indexed in Scilit:
- Classification of Highly Unbalanced CYP450 Data of Drugs Using Cost Sensitive Machine Learning TechniquesJournal of Chemical Information and Modeling, 2006
- Substructure-Based Support Vector Machine Classifiers for Prediction of Adverse Effects in Diverse Classes of DrugsJournal of Chemical Information and Modeling, 2006
- Semiempirical Quantum Chemical Method and Artificial Neural Networks Applied for λmax Computation of Some Azo DyesJournal of Chemical Information and Computer Sciences, 2004
- New Approach by Kriging Models to Problems in QSARJournal of Chemical Information and Computer Sciences, 2004
- Development of Linear, Ensemble, and Nonlinear Models for the Prediction and Interpretation of the Biological Activity of a Set of PDGFR InhibitorsJournal of Chemical Information and Computer Sciences, 2004
- Effect of Molecular Descriptor Feature Selection in Support Vector Machine Classification of Pharmacokinetic and Toxicological Properties of Chemical AgentsJournal of Chemical Information and Computer Sciences, 2004
- A Comparative Study on Feature Selection Methods for Drug DiscoveryJournal of Chemical Information and Computer Sciences, 2004
- Using Ensembles to Classify Compounds for Drug DiscoveryJournal of Chemical Information and Computer Sciences, 2003
- Predictive Toxicology: Benchmarking Molecular Descriptors and Statistical MethodsJournal of Chemical Information and Computer Sciences, 2003
- Wrappers for feature subset selectionArtificial Intelligence, 1997