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SUMMARY:Knowledge Graphs for Precision Oncology - Krishna C Bulusu Directo
 r\, Early Data Science Oncology Data Science\, Oncology R&amp\;D AstraZene
 ca
DTSTART:20221116T140000Z
DTEND:20221116T150000Z
UID:TALK182891@talks.cam.ac.uk
CONTACT:Samantha Noel
DESCRIPTION:Knowledge Graphs have in recent years gained a lot of prominen
 ce within biomedical AI and applications. This partnership holds tremendou
 s potential given the highly complex and sparse nature of biomedical data\
 , along with the need for prior knowledge to be integrated with the world
 ’s knowledge to obtain a deep and comprehensive view of complex disease 
 landscapes such as in Oncology. The ‘unFAIR’ (Findable\, Accessible\, 
 Interoperable\, Reusable) nature of this research field makes healthcare A
 I technically and scientifically challenging\, and Knowledge Graphs driven
  by NLP and GraphML (Graph Machine Learning) could greatly influence the d
 rug discovery and development processes.\n\nIn this talk\, I will discuss 
 AstraZeneca’s Knowledge Graph (BIKG) focussing on a selection of real-wo
 rld applications answering day-to-day drug discovery questions. These use 
 cases cover accelerating target discovery to predicting drug efficacy in d
 isease models. In addition\, I will also discuss how Graphs can help bridg
 e the critical bench-to-bedside gap in biomedical R&D by translating Disco
 very knowledge to Clinical applications\, and vice versa. I will conclude 
 by discussing key bottlenecks and pain-points prevalent in the scientific 
 community that will need to be addressed for Knowledge Graphs to drive and
  influence key decisions in the drug discovery pipeline.\n\n
LOCATION:CMS\, Meeting Room 15
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