biostudies-literatureGao ZSupport Project of Gansu CollegesGansu Natural Science Foundation ProjectNational Natural Science Foundation of Chinabtad357https://www.ebi.ac.uk/biostudies/studies/S-EPMC10275904biostudies-literatureUnknown39(6)<h4>Motivation</h4>An imperative step in drug discovery is the prediction of drug-disease associations (DDAs), which tries to uncover potential therapeutic possibilities for already validated drugs. It is costly and time-consuming to predict DDAs using wet experiments. Graph Neural Networks as an emerging technique have shown superior capacity of dealing with DDA prediction. However, existing Graph Neural Networks-based DDA prediction methods suffer from sparse supervised signals. As graph contrastive learning has shined in mitigating sparse supervised signals, we seek to leverage graph contrastive learning to enhance the prediction of DDAs. Unfortunately, most conventional graph contrastive learning-based models corrupt the raw data graph to augment data, which are unsuitable for DDA predBioinformatics (Oxford, England)Similarity measures-based graph co-contrastive learning for drug-disease association prediction.PMC102759046176207821JR7RA11461363058Wu ZZhang XMa HGao ZWang YfalseSimilarity measures-based graph co-contrastive learning for drug-disease association prediction.<h4>Motivation</h4>An imperative step in drug discovery is the prediction of drug-disease associations (DDAs), which tries to uncover potential therapeutic possibilities for already validated drugs. It is costly and time-consuming to predict DDAs using wet experiments. Graph Neural Networks as an emerging technique have shown superior capacity of dealing with DDA prediction. However, existing Graph Neural Networks-based DDA prediction methods suffer from sparse supervised signals. As graph contrastive learning has shined in mitigating sparse supervised signals, we seek to leverage graph contrastive learning to enhance the prediction of DDAs. Unfortunately, most conventional graph contrastive learning-based models corrupt the raw data graph to augment data, which are unsuitable for DDA pred2023-01-01T00:00:00Z2023 Jun2025-04-22T01:57:45.244Z2025-04-05T20:14:03.415ZS-EPMC102759043726185910.1093/bioinformatics/btad357