ArticleScience advances2024
Phenome-wide identification of therapeutic genetic targets, leveraging knowledge graphs, graph neural networks, and UK Biobank data.
Article in Science advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Genetically supported drug target prioritization for rare diseases.Genome medicine · 2026Article
- Genomics of drug target prioritization for complex diseases.Nature reviews. Genetics · 2026Review
- The impact of artificial intelligence on biomarker discovery.Emerging topics in life sciences · 2025Article
- Universal multilayer network embedding reveals a causal link between GABA neurotransmitter and cancer.BMC bioinformatics · 2025Article
- Haploinsufficiency of ITSN1 is associated with a substantial increased risk of Parkinson's disease.Cell reports · 2025Article
- Genetic evidence informs the direction of therapeutic modulation in drug development.npj drug discovery · 2025Article
- Longitudinal clinical and proteomic diabetes signatures in women with a history of gestational diabetes.JCI insight · 2024Article
- Genome-wide association neural networks identify genes linked to family history of Alzheimer's disease.Briefings in bioinformatics · 2024Article
- Disease prediction with multi-omics and biomarkers empowers case-control genetic discoveries in the UK Biobank.Nature genetics · 2024Article
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The ongoing expansion of human genomic datasets propels therapeutic target identification; however, extracting gene-disease associations from gene annotations remains challenging. Here, we introduce Mantis-ML 2.0, a framework integrating AstraZeneca's Biological Insights Knowledge Graph and numerous tabular datasets, to assess gene-disease probabilities throughout the phenome. We use graph neural networks, capturing the graph's holistic structure, and train them on hundreds of balanced datasets via a robust semi-supervised learning framework to provide gene-disease probabilities across the human exome. Mantis-ML 2.0 incorporates natural language processing to automate disease-relevant feature selection for thousands of diseases. The enhanced models demonstrate a 6.9% average classification power boost, achieving a median receiver operating characteristic (ROC) area under curve (AUC) score of 0.90 across 5220 diseases from Human Phenotype Ontology, OpenTargets, and Genomics England. Notably, Mantis-ML 2.0 prioritizes associations from an independent UK Biobank phenome-wide association study (PheWAS), providing a stronger form of triaging and mitigating against underpowered PheWAS associations. Results are exposed through an interactive web resource.
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Registered trials
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