Structure-activity relationship-based chemical classification of highly imbalanced Tox21 datasets.

ABSTRACT: The specificity of toxicant-target biomolecule interactions lends to the very imbalanced nature of many toxicity datasets, causing poor performance in Structure-Activity Relationship (SAR)-based chemical classification. Undersampling and oversampling are representative techniques for handling such an imbalance challenge. However, removing inactive chemical compound instances from the majority class using an undersampling technique can result in information loss, whereas increasing active toxicant instances in the minority class by interpolation tends to introduce artificial minority instances that often cross into the majority class space, giving rise to class overlapping and a higher false prediction rate. In this study, in order to improve the prediction accuracy of imbalanced learning,

SUBMITTER: Idakwo G 

PROVIDER: S-EPMC7592558 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature