ArticleCommunications biology2022
LeMeDISCO is a computational method for large-scale prediction & molecular interpretation of disease comorbidity.
Article in Communications biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
- PHENOCAUZ: Linking Human Symptoms, Drug Side Effects and Efficacy to Their Molecular Causes Using Mendelian Disease Biology.bioRxiv : the preprint server for biology · 2026Article
- Graph transformer with disease subgraph positional encoding for improved comorbidity prediction.Quantitative biology (Beijing, China) · 2025Article
- Patient stratification reveals the molecular basis of disease co-occurrences.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- A mode of action protein based approach that characterizes the relationships among most major diseases.Scientific reports · 2025Article
- Network Pharmacology Approaches Used to Identify Therapeutic Molecules for Chronic Venous Disease Based on Potential miRNA Biomarkers.Journal of xenobiotics · 2024Article
- PHEVIR: an artificial intelligence algorithm that predicts the molecular role of pathogens in complex human diseases.Scientific reports · 2022Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
To understand the origin of disease comorbidity and to identify the essential proteins and pathways underlying comorbid diseases, we developed LeMeDISCO (Large-Scale Molecular Interpretation of Disease Comorbidity), an algorithm that predicts disease comorbidities from shared mode of action proteins predicted by the artificial intelligence-based MEDICASCY algorithm. LeMeDISCO was applied to predict the occurrence of comorbid diseases for 3608 distinct diseases. Benchmarking shows that LeMeDISCO has much better comorbidity recall than the two molecular methods XD-score (44.5% vs. 6.4%) and the S
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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.