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SUMMARY:Infusing Structure and Knowledge Into Biomedical AI Algorithms - D
 r Marinka Zitnik
DTSTART:20211125T150000Z
DTEND:20211125T160000Z
UID:TALK162820@talks.cam.ac.uk
CONTACT:Sarah Morgan
DESCRIPTION:Grand challenges in biology and medicine often lack annotated 
 examples and require generalization to entirely new scenarios not seen dur
 ing training. However\, standard supervised learning is incredibly limited
  in scenarios\, such as designing novel medicines\, modeling emerging path
 ogens\, and treating rare diseases. In this talk\, I present our efforts t
 o overcome these obstacles by infusing structure and knowledge into learni
 ng algorithms. First\, I outline our subgraph neural networks that disenta
 ngle distinct aspects of subgraph structure in networks. I will then prese
 nt a general-purpose approach for few-shot learning on graphs. At the core
  is the notion of local subgraphs that transfer knowledge from one task to
  another\, even when only a handful of labeled examples are available. Thi
 s principle is theoretically justified as we show the evidence for predict
 ions can be found in subgraphs surrounding the targets. I will conclude wi
 th applications in drug development and precision medicine where the algor
 ithmic predictions were validated in human cells and led to the discovery 
 of a new class of drugs.
LOCATION:Online
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