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SUMMARY:Symbolic AI in Computational Biology\; applications to disease gen
 e and drug target identification - Prof Robert Hoehndorf\, King Abdullah U
 niversity of Science and Technology\, KSA
DTSTART:20180226T163000Z
DTEND:20180226T173000Z
UID:TALK98866@talks.cam.ac.uk
CONTACT:Almarie Williams
DESCRIPTION:The life sciences have invested significant resources in the d
 evelopment 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 analy
 sis approaches is often challenging. Now\, novel methods are becoming avai
 lable that combine symbolic methods and statistical methods in Artificial 
 Intelligence. In my talk\, I will describe how to apply knowledge graph em
 beddings for analysis of biological and biomedical data\, in particular id
 entification of gene-disease associations and drug targets. I will also sh
 ow how information from text-mining can be combined in a multi-modal machi
 ne learning model to further improve predictive performance of these model
 s\, and how these methods can help to improve interpretation of causative 
 genomic variants in personal genomic sequence data.
LOCATION:Seminar Room MR4\, Centre for Mathematical Sciences\, Wilberforce
  Road\, Cambridge
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