Enhancing Drug Repositioning through Contrastive Learning and Denoised Negative Sampling

Le résumé fourni par la source

Drug repositioning is a promising strategy for treating diseases by repurposing safe drugs, which reduces development costs and timelines. Drug-disease association (DDA) plays a crucial role in this strategy. Although existing DDA models have made significant progress, there is a need for improvement in integrating diverse information and creating better training data. In this paper, we propose a novel method, termed DNSDDA that utilize the contrast learning and denoised negative sampling strategy for drug-disease association prediction. Firstly, DNSDDA constructs semantic networks from multiple data sources and meta-path networks from topological information for drugs and diseases. Then DNSDDA enhances topological embeddings of drugs and diseases by infusing semantic information through contrastive learning in the node representation of the meta-path network. After that, we employed denoised negative sampling to enhance the training set quality. Lastly, DNSDDA trains a binary classifier using learned embeddings of drugs and diseases for predicting potential associations. The experiment results demonstrate that BiGNN achieves outperforming performance with the average AUPROC of 0.947 and AUPR of 0.804. when evaluating on two benchmark datasets by 10-fold cross-validation.

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