Abstract
Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval, and federated queries. In the past years, feature learning methods that are applicable to graph-structured data are becoming available, but have not yet widely been applied and evaluated on structured biological knowledge. We develop novel methods for feature learning on biological knowledge graphs and apply them to the prediction of edges in the knowledge graph representing problems of finding candidate genes of diseases, or drugs repurposing, and demonstrate performance that matches and sometimes outperforms traditional approaches based on manually crafted features.
Brief Biography
Mona Alshahrani joined KAUST in 2013 and completed her M.Sc degree in 2015. Before that, she was a teaching assistant at Jubail University College, Jubail, Saudi Arabia. Currently, she is a Ph.D. student at the Bio-Ontology Research Group (BORG) focusing on bioinformatics, data integration, machine/deep learning methods in knowledge graphs and their applications to human healthcare.
For more info contact: Robert Hoehndorf - email: robert.hoehndorf@kaust.edu.sa
Refreshments: Light lunch will be provided
