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 apply knowledge-based methods for the analysis of biological and biomedical data, in particular identification of gene-disease associations and drug targets.
Robert Hoehndorf is an Assistant Professor in Computer Science at King Abdullah University of Science and Technology (KAUST) in Thuwal. Prior to joining KAUST, Robert had research positions at Aberystwyth University, the University of Cambridge, the European Bioinformatics Institute, and the Max Planck Institute for Evolutionary Anthropology. His research focuses on the applications of knowledge-based algorithms in biology and biomedicine, with a particular emphasis on integrating and analyzing heterogeneous, multimodal data. Robert is an associate editor for the Journal of Biomedical Semantics, BMC Bioinformatics, Applied Ontology, PLoS ONE, and editorial board member of the journal Data Science. He published over 100 research papers in journals and international conferences.