Bayesian Inference for Drug Discovery by High Negative Samples and Oversampling.

Abstract

Drug repositioning holds great promise for reducing the time and cost associated with traditional drug discovery, but it faces significant challenges related to data imbalance and noise in negative samples. In this article, we introduce a novel method leveraging high negative oversampling (HNO) to address these challenges. Our approach integrates HNO with advanced techniques such as network-based graph mining, matrix factorization, and Bayesian inference, specifically designed for imbalanced data scenarios. Constructing high-quality negative samples is crucial to mitigate the detrimental effects of noisy negative data and enhance model performance. Experimental results demonstrate the efficacy of our approach in enhancing the performance of drug discovery models by effectively managing data imbalance and refining the selection of negative samples. This methodology provides a robust framework for improving drug repositioning, with potential applications in broader biomedical domains.