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LOCATION:HG F 26.5
DTSTART;TZID=Europe/Stockholm:20240603T113000
DTEND;TZID=Europe/Stockholm:20240603T133000
UID:submissions.pasc-conference.org_PASC24_sess142@linklings.com
SUMMARY:MS1H - Supercomputing for the Drug Response Prediction Community
DESCRIPTION:Minisymposium\n\nThe minisymposium will offer an opportunity f
 or experts in scientific computing and life sciences to share knowledge su
 rrounding the challenging task of comparing machine learning models for ca
 ncer drug response prediction. The minisymposium, which will be presented 
 by a range of cancer scientists and computer scientists, will provide an o
 verview of cancer drug response prediction, and the computing challenges t
 hat are posed by this problem.   Two presenters will cover the development
  of drug response models.  These will be drawn from the community of model
  developers who produce models that are now available for comparison.  Two
  other presenters will cover the usage of drug response models.  These wil
 l be drawn from the community of stakeholders that use cancer models in br
 oader research initiatives in cancer science and the development of treatm
 ents.  They will describe how their team uses computational and data produ
 cts, how they interact with developers, and what the future of drug respon
 se prediction may hold.  This minisymposium is not simply about cancer pre
 diction, as the collection of models that is emerging is a valuable asset 
 to the machine learning community, and may be used for a range of studies 
 in machine learning systems, performance, accuracy, and other behavior.\n\
 nFoundational Models and Workflows: Enhancing Deep Learning Comparisons in
  Drug Response Studies\n\nIn the evolving field of computational drug desi
 gn and discovery, the accurate prediction of drug responses through deep l
 earning models remains a significant challenge due to varying methodologie
 s in model implementation and validation. This inconsistency hampers the o
 bjective assessment of model c...\n\n\nNeeraj Kumar (Pacific Northwest Nat
 ional Laboratory)\n---------------------\nTowards an Open Ecosystem for FA
 IR (Findable, Accessible, Interoperable, Reusable) Drug Response Predictio
 n Models and Data\n\nFueled by pervasive supercomputing and growing availa
 bility of response data have led to a dramatic increase in the availabilit
 y of predictive drug response models. Over the past several years, the col
 laboration between the US Department of Energy and the US National Cancer 
 Institute have resulted i...\n\n\nEric Stahlberg (Frederick National Labor
 atory for Cancer Research)\n---------------------\nNavigating the Future o
 f AI in Personalized Medicine: Challenges and Innovations\n\nThis panel di
 scussion will explore the landscape of artificial intelligence in personal
 ized medicine, focusing on the development, integration, and regulatory ch
 allenges of AI models designed to predict tumor responses, treatment toxic
 ities, and other related responses. Experts will discuss the sust...\n\n\n
 Justin Wozniak (Argonne National Laboratory), Neeraj Kumar (Pacific Northw
 est National Laboratory), and Eric Stahlberg (Frederick National Laborator
 y for Cancer Research)\n---------------------\nInfrastructure to Support a
  Community of Drug Response Prediction Modelers\n\nThe intersection of pre
 cision medicine and machine learning (ML) offers a wide range of problems 
 and possible approaches.  The prediction of tumor response to single and c
 ombination drug agents is an active area of ML application development, as
  tens of deep learning models are currently available f...\n\n\nJustin Woz
 niak (Argonne National Laboratory, University of Chicago)\n\nDomain: Life 
 Sciences, Computational Methods and Applied Mathematics\n\nSession Chair: 
 Thomas Brettin (Argonne National Laboratory, University of Chicago)
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