Learning from semantic biological data

 

Monday, 10/22/18 – 1-2pm – 366 WVH

 

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, and how these methods can help to improve the

prediction of protein functions.

 

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 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.