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
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...
May 10, 2024: Science Advances