| Title: | Phenome-wide identification of therapeutic genetic targets, leveraging knowledge graphs, graph neural networks, and UK Biobank data |
| Journal: | Science Advances |
| Published: | 8 May 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38718126/ |
| DOI: | https://doi.org/10.1126/sciadv.adj1424 |
| URL: | https://www.science.org/doi/pdf/10.1126/sciadv.adj1424?download=true |
| Citations: | 1 (1 in last 2 years) as of 8 Aug 2024 |
Publication 12127
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
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.</p>
13 Keywords
- Algorithms
- Biological Specimen Banks
- Computational Biology
- Databases, Genetic
- Genetic Predisposition to Disease
- Genome-Wide Association Study
- Genomics
- Humans
- Neural Networks, Computer
- Phenomics
- Phenotype
- UK Biobank
- United Kingdom
13 Authors
- Lawrence Middleton
- Ioannis Melas
- Chirag Vasavda
- Arwa Raies
- Benedek Rozemberczki
- Ryan S. Dhindsa
- Justin S. Dhindsa
- Blake Weido
- Quanli Wang
- Andrew R. Harper
- Gavin Edwards
- Slavé Petrovski
- Dimitrios Vitsios
Enabling scientific discoveries that improve human health