The life sciences have invested significant resources in the development and application of semantic technologies to make research data accessible and interlinked, and to enable the integration and analysis of data. Utilizing the semantics associated with research data in data analysis approaches is often challenging. Now, novel methods are becoming available that combine symbolic methods and statistical methods in Artificial Intelligence. In my talk, I will describe how to combine symbolic and statistical Artificial Intelligence approaches for the analysis of biological and biomedical data. I will focus on the identification of gene-disease associations, interpretation of causative variants, and prediction of protein functions.
Robert Hoehndorf is an Assistant Professor in Computer Science at King Abdullah University of Science and Technology in Thuwal. His research focuses on the applications of symbolic AI in biology and biomedicine, with a particular emphasis on integrating and analyzing heterogeneous, multimodal data. Robert has contributed to the PhenomeNET system for ontology-based prioritization of disease genes using model organism phenotypes, the AberOWL ontology repository, and the DeepGO system for protein function prediction. He is an associate editor for the Journal of Biomedical Semantics, BMC Bioinformatics, Applied Ontology, and editorial board member of the journal Data Science. He published over 90 papers in journals and international conferences.
For more info contact: Professor Robert Hoehndorf: email: firstname.lastname@example.org
Refreshments: Light Lunch will be available at 11:45 am