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SUMMARY:Generative machine learning to model cellular perturbations  - Moh
 ammad Lotfollahi
DTSTART:20250224T123000Z
DTEND:20250224T133000Z
UID:TALK226087@talks.cam.ac.uk
CONTACT:126982
DESCRIPTION:The field of cellular biology has long sought to understand th
 e intricate mechanisms that govern cellular responses to various perturbat
 ions\, be they chemical\, physical\, or biological. Traditional experiment
 al approaches\, while invaluable\, often face limitations in scalability a
 nd throughput\, especially when exploring the vast combinatorial space of 
 potential cellular states. Enter generative machine learning that has show
 n exceptional promise in modeling complex biological systems. This talk wi
 ll highlight recent successes\, address the challenges and limitations of 
 current models\, and discuss the future direction of this exciting interdi
 sciplinary field. Through examples of practical applications\, we will ill
 ustrate the transformative potential of generative ML in advancing our und
 erstanding of cellular perturbations and in shaping the future of biomedic
 al research.
LOCATION:CRUK CI Lecture Theatre
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