[Colloq] Time Correction: (2-3pm) Seminar: Learning from semantic biological data - Monday, 10/22/18 - 2-3pm - 366 WVH

[Ponte, Christopher]

Learning from semantic biological data

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.