@article{4096,
  author = {Lawrence Middleton and Ioannis Melas and Chirag Vasavda and Arwa Raies and Benedek Rozemberczki and Ryan S. Dhindsa and Justin S. Dhindsa and Blake Weido and Quanli Wang and Andrew R. Harper and Gavin Edwards and Slavé Petrovski and Dimitrios Vitsios},
  title = {Phenome-wide identification of therapeutic genetic targets, leveraging knowledge graphs, graph neural networks, and UK Biobank data},
  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.},
  year = {2024},
  journal = {Science Advances},
  volume = {10},
  pages = {eadj1424},
  month = {2024-05-08},
  url = {https://www.science.org/doi/10.1126/sciadv.adj1424},
  doi = {10.1126/sciadv.adj1424},
}