Project Leader: Rafael Goncalves, PhD
Artificial Intelligence applications require Knowledge Representation and Reasoning to encode knowledge in a way that computers can reason with. The Knowledge Representation group at CCB develops knowledge representation and reasoning solutions to facilitate the discovery, integration and meta-analysis of biomedical data originating from diverse sources. The data include electronic health records, healthcare insurance claims, genomics, environmental exposure, among others. The integration of these data will support large-scale epidemiological research in precision medicine, healthcare, and basic science, all with the goal of improving patient outcomes.
Our current projects revolve around the use of ontologies—computational artifacts that provide standardized symbols to represent phenotypes, diseases, gene products (entities), and that describe the relationships that exist between those entities. We adopt an open-development model in our projects, and all the resources that we develop are open-source. Learn about our ongoing and previous projects below!