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DTSTART;TZID=America/Chicago:20251117T092400
DTEND;TZID=America/Chicago:20251117T094200
UID:submissions.supercomputing.org_SC25_sess208_ws_worksp121@linklings.com
SUMMARY:A Workflow for Error Analysis for Drug Response Prediction via Sta
 tistical Standardization and Distribution Analysis
DESCRIPTION:Jake Gwinn (University of Michigan); Justin Wozniak (Argonne N
 ational Laboratory (ANL), University of Chicago); and Rajeev Jain, Yitan Z
 hu, Alex Partin, Thomas Brettin, and Rick Stevens (Argonne National Labora
 tory (ANL))\n\nDrug response prediction is a promising approach to apply m
 achine learning to the development of drugs for a range of cancer types.  
 This method can be used to pre-screen potential drugs, perform high-throug
 hput screening of drug databases, or perform more generalized tasks in mac
 hine learning.  In an idealized real-world clinical situation, the overall
  solution must produce a short list of the most promising drugs for a part
 icular patient medical situation.  Promising drugs for a given case, howev
 er, are very rare, making model performance in this space very difficult. 
  Thus, a great deal of supporting infrastructure must be developed to make
  this possible, including obtaining and curating datasets, large cross-val
 idation training studies, and post-training inference and analysis.  Herei
 n, we describe a new approach for dealing with the rare drug problem, and 
 implement a portable workflow that explores one proposed strategy for addr
 essing it, with results from the exascale supercomputer Aurora.\n\nRecordi
 ng: Livestreamed, Recorded\n\nRegistration Category: Technical Program Reg
  Pass, Workshop Reg Pass\n\nSession Chairs: Silvina Caino-Lores (National 
 Institute for Research in Digital Science and Technology (Inria)) and Anir
 ban Mandal (Renaissance Computing Institute (RENCI), University of North C
 arolina at Chapel Hill)\n\n
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