Improving biological mechanisms understanding through explainable reinforcement learning of user preferences.

The Biomedical Data Translator project (NIH/NCATS) seeks to revolutionize disease biology understanding by developing a robust and interpretable reseaning framework that translates complex biomedical data into actionable insights. In a federated and distributed framework, the platform makes recommendations of biological mechanisms involved in diseases to researchers from different disciplines. AI models analyze data across multiple sources, including clinical trials, genomic sequencing, proteomics, metabolomics, etc and provide an explanation of their reasoning that led to the recommendations.

The project aims to improve the recommandations quality by integrating user preferences through reinforcement learning techniques. This project has the potential to identify novel therapeutic targets through drug repurposing and uncover underlying biological mechanisms involved in diseases.