A seminar will be given at CMS by Prof Robert Hoehndorf who will be visiting Cambridge in February;
Title: Symbolic AI in Computational Biology; applications to disease gene and drug target identification
Speaker: Prof Robert Hoehndorf, King Abdullah University of Science and Technology
Location: Centre for Mathematical Sciences, Seminar Room MR4 26th February, 16:30
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. 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 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 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 in 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 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.
