Rethinking evaluation for multi-label drug-drug interaction prediction

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References

[1] K antor E D, R ehm C D, H aas J S, C han A T, G iovannucci E L . Trends in prescription drug use among adults in the United States from 1999−2012. JAMA, 2015, 314( 17): 1818–1830

[2] M arengoni A, P asina L, C oncoreggi C, M artini G, B rognoli F, N obili A, O nder G, B ettoni D . Understanding adverse drug reactions in older adults through drug–drug interactions. European Journal of Internal Medicine, 2014, 25( 9): 843–846

[3] Z hao H, Z hong J, L iang X, X ie C, W ang S . Application of machine learning in drug side effect prediction: databases, methods, and challenges. Frontiers of Computer Science, 2025, 19( 5): 195902

[4] Karim R, Cochez M, Jares J B, Uddin M, Beyan O, Decker S. Drug-drug interaction prediction based on knowledge graph embeddings and convolutional-LSTM network. In: Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. 2019, 113−123

[5] H uang K, X iao C, G lass L M, Z itnik M, S un J . SkipGNN: predicting molecular interactions with skip-graph networks. Scientific Reports, 2020, 10( 1): 21092

[6] Z itnik M, A grawal M, L eskovec J . Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics, 2018, 34( 13): i457–i466

[7] Y u Y, H uang K, Z hang C, G lass L M, S un J, X iao C . SumGNN: multi-typed drug interaction prediction via efficient knowledge graph summarization. Bioinformatics, 2021, 37( 18): 2988–2995

[8] W ang Y, Y ang Z, Y ao Q . Accurate and interpretable drug-drug interaction prediction enabled by knowledge subgraph learning. Communications Medicine, 2024, 4( 1): 59

[9] Z hang Y, Y ao Q, Y ue L, W u X, Z hang Z, L in Z, Z heng Y . Emerging drug interaction prediction enabled by a flow-based graph neural network with biomedical network. Nature Computational Science, 2023, 3( 12): 1023–1033

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