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SUMMARY:Caroline Uhler (MIT and Institute for Data\, Systems and Society)
DTSTART:20210210T180000Z
DTEND:20210210T190000Z
DTSTAMP:20260423T003246Z
UID:MPML/28
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/MPML/28/">Ca
 usal Inference and Overparameterized Autoencoders in the Light of Drug Rep
 urposing for SARS-CoV-2</a>\nby Caroline Uhler (MIT and Institute for Data
 \, Systems and Society) as part of Mathematics\, Physics and Machine Learn
 ing (IST\, Lisbon)\n\n\nAbstract\nMassive data collection holds the promis
 e of a better understanding of complex phenomena and ultimately\, of bette
 r decisions. An exciting opportunity in this regard stems from the growing
  availability of perturbation / intervention data (drugs\, knockouts\, ove
 rexpression\,\netc.) in biology. In order to obtain mechanistic insights f
 rom such data\, a major challenge is the development of a framework that i
 ntegrates observational and interventional data and allows predicting the 
 effect of yet unseen interventions or transporting the effect of intervent
 ions observed in one context to another. I will present a framework for ca
 usal structure discovery based on such data and highlight the role of over
 parameterized autoencoders. We end by demonstrating how these ideas can be
  applied for drug repurposing in the current SARS-CoV-2 crisis.\n
LOCATION:https://researchseminars.org/talk/MPML/28/
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