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SUMMARY:Biological design with machine learning and limited data. - Diego 
 Oyarzun\, University of Edinburgh
DTSTART:20250313T140000Z
DTEND:20250313T150000Z
UID:TALK229288@talks.cam.ac.uk
CONTACT:Fulvio Forni
DESCRIPTION:AI and machine learning have rapidly emerged as promising tool
 s for cellular engineering and optimisation. Yet the complexities of biolo
 gical measurements often limit the applicability of state-of-the-art algor
 ithms that require large and well-curated data for training. This gap coul
 d potentially leave behind many academic and industry laboratories that co
 uld hugely benefit from this technology. In this talk\, I will describe re
 cent applications of machine learning for in silico discovery and optimisa
 tion\, with a focus on small and heterogeneous datasets typically encounte
 red in biological design tasks. Examples include predicting protein expres
 sion/function from sequence information\, low-N drug discovery against com
 plex diseases\, and optimisation of gene circuits for metabolite productio
 n.\n\nThe seminar will be held in LR3A\, Department of Engineering\, and o
 nline (zoom): https://newnham.zoom.us/j/92544958528?pwd=YS9PcGRnbXBOcStBdS
 tNb3E0SHN1UT09
LOCATION:LR3A\, Department of Engineering and online (Zoom)
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