ArticleBioinformatics (Oxford, England)2023
Multimodal representation learning for predicting molecule-disease relations.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 21 citations in OpenAlex.
- Impact of molecular multimodality on neural network models for prediction tasks related to drug discovery.Nature communications · 2026Article
- Phenotypic prediction of missense variants via deep contrastive learning.Nature biomedical engineering · 2026Article
- Representation learning to advance multi-institutional studies with electronic health record data from US and France.Nature communications · 2026Article
- Inference of dependency knowledge graph for Electronic Health Records.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2026Article
- RNA-KG v2.0: an RNA-centered Knowledge Graph with Properties.NAR genomics and bioinformatics · 2026Article
- Artificial Intelligence for Drug Safety Across the Lifecycle and Decision Type: A Scoping Review.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Leveraging large-scale biobanks for therapeutic target discovery.HGG advances · 2026Article
- Advancing Drug Discovery with Enhanced Chemical Understanding via Asymmetric Contrastive Multimodal Learning.Journal of chemical information and modeling · 2025Article
- Pretraining graph transformers with atom-in-a-molecule quantum properties for improved ADMET modeling.Journal of cheminformatics · 2025Article
- ARCH: Large-scale knowledge graph via aggregated narrative codified health records analysis.Journal of biomedical informatics · 2025Article
- DOME: Directional medical embedding vectors from Electronic Health Records.Journal of biomedical informatics · 2025Article
- Recent advances in AI-based toxicity prediction for drug discovery.Frontiers in chemistry · 2025Review
- Graph Artificial Intelligence in Medicine.Annual review of biomedical data science · 2024Review
- LATTE: Label-efficient incident phenotyping from longitudinal electronic health records.Patterns (New York, N.Y.) · 2024Article
- Current and future directions in network biology.Bioinformatics advances · 2024Article
- AI-Based Computational Methods in Early Drug Discovery and Post Market Drug Assessment: A Survey.IEEE transactions on computational biology and bioinformaticsReview
Corrections and comments
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Authors and funding
17 authors at 7 institutions in 1 country.
Funding
Abstract
motivationPredicting molecule-disease indications and side effects is important for drug development and pharmacovigilance. Comprehensively mining molecule-molecule, molecule-disease and disease-disease semantic dependencies can potentially improve prediction performance.
methodsWe introduce a Multi-Modal REpresentation Mapping Approach to Predicting molecular-disease relations (M2REMAP) by incorporating clinical semantics learned from electronic health records (EHR) of 12.6 million patients. Specifically, M2REMAP first learns a multimodal molecule representation that synthesizes chemical property and clinical semantic information by mapping molecule chemicals via a deep neural network onto the clinical semantic embedding space shared by drugs, diseases and other common clinical concepts. To infer molecule-disease relations, M2REMAP combines multimodal molecule representation and disease semantic embedding to jointly infer indications and side effects.
resultsWe extensively evaluate M2REMAP on molecule indications, side effects and interactions. Results show that incorporating EHR embeddings improves performance significantly, for example, attaining an improvement over the baseline models by 23.6% in PRC-AUC on indications and 23.9% on side effects. Further, M2REMAP overcomes the limitation of existing methods and effectively predicts drugs for novel diseases and emerging pathogens. AVAILABILITY AND IMPLEMENTATION: The code is available at https://github.com/celehs/M2REMAP, and prediction results are provided at https://shiny.parse-health.org/drugs-diseases-dev/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.