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DTSTAMP:20260422T000711Z
LOCATION:DEF Concourse
DTSTART;TZID=America/Denver:20231114T100000
DTEND;TZID=America/Denver:20231114T170000
UID:submissions.supercomputing.org_SC23_sess291_rpost186@linklings.com
SUMMARY:Developing an Inverse Reinforcement Learning Methodology to Predic
 t the Progression of Colorectal Cancer
DESCRIPTION:Silba Dowell, Daniel Hintz, Tyson Limato, Shad Sellers, and Mi
 lana Wolff (University of Wyoming); Nicholas Chia (Argonne National Labora
 tory (ANL)); and Liudmila Mainzer (University of Wyoming)\n\nIn cancer bio
 logy, large amounts of high dimensional data (genomic, transcriptomic, pro
 teomic, phenotypic, etc.) are required for any computationally relevant wo
 rk. The problem is further complicated by the sheer size of the human geno
 me, roughly three billion base pairs long. Therefore, computation is time-
 consuming and data-intensive. To solve this problem for human colorectal c
 ancer, we are implementing a machine learning engine based on inverse rein
 forcement learning, and includes several different kinds of neural network
 s to perform data preparation, training, and prediction. Our work aims to 
 reconstruct the progression of tumor development in a sample, and predict 
 the next steps of its evolution, to aid in diagnosis and treatment. This p
 oster will be presented as a work in progress methodology.\n\nTag: Artific
 ial Intelligence/Machine Learning, Architecture and Networks, Heterogeneou
 s Computing, I/O and File Systems, Performance Measurement, Modeling, and 
 Tools, Post-Moore Computing, Programming Frameworks and System Software, Q
 uantum Computing\n\nRegistration Category: Tech Program Reg Pass, Exhibits
  Reg Pass\n\n
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