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SUMMARY:Math to Medicines: Accelerating Drug Discovery\, Development and R
 epositioning using Clinical Trial Data Mining and Machine Intelligence - S
 hameer Khader\, AstraZeneca
DTSTART:20220210T160000Z
DTEND:20220210T170000Z
UID:TALK169790@talks.cam.ac.uk
CONTACT:Stephanie North
DESCRIPTION:The drug development process is a time-consuming and expensive
  endeavor. The application of data science and artificial intelligence met
 hods could potentially improve drug discovery\, development\, and repositi
 oning. Collectively\, the application of big data tools and methods could 
 help handle the high dimensionality of the data and help develop novel mod
 elling strategies to link early experimental analysis to clinical outcomes
 . In this talk\, I will discuss three key ideas: novel methods for clinica
 l trial data mining (SAEgnal\, TrialGraph\, ClinicalTrials2Vec)\, multi-om
 ics data integration (Omicsfold and BlockRank) and digital drug reposition
 ing. Drug development teams could use the collective insights and models t
 o augment trials using data-driven approaches\, improve patient engagement
 \, optimize side effects\, uncover novel indications\, and ultimately acce
 lerate drug discovery.
LOCATION:https://maths-cam-ac-uk.zoom.us/j/93058414146?pwd=SW8yM05pVm9oVXN
 FSnZtWW8xLzQ3QT09
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