Robert Hoehndorf, Assistant Professor of Computer Science at King Abdullah University of Science and Technology in Thuwal
 

Abstract: 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. Utilising the semantics associated with research data in data analysis approaches is often challenging. Recently, as a consequence of the success of machine learning and statistical methods in data analysis, novel methods are becoming available that combine symbolic methods and statistical methods in Artificial Intelligence. In my talk, I will describe how to apply knowledge graph embeddings for analysis of biological and biomedical data, in particular, identification of gene-disease associations and drug targets. I will also show how information from text-mining can be combined in a multi-modal machine learning model to further improve the predictive performance of these models, and how these methods can help to improve interpretation of causative genomic variants in personal genomic sequence data. 

Bio: Robert Hoehndorf is an Assistant Professor of Computer Science at King Abdullah University of Science and Technology in Thuwal. His research focuses on the applications of ontologies in biology and biomedicine, with a particular emphasis on integrating and analysing heterogeneous, multimodal data. Robert has developed the PhenomeNET system for ontology-based prioritisation of disease genes using model organism phenotypes and contributed to the development of the AberOWL ontology repository. He is an associate editor of the Journal of Biomedical Semantics and editorial board member of the IOS press journal Data Science. He published over 80 papers in journals and international conferences.