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DTSTART;TZID=America/New_York:20241118T140000
DTEND;TZID=America/New_York:20241118T173000
UID:submissions.supercomputing.org_SC24_sess818@linklings.com
SUMMARY:Tenth Computational Approaches for Cancer Workshop
DESCRIPTION:Lightning Round Virtual Poster Session and Break\n\nLauren Lew
 is (Frederick National Laboratory for Cancer Research)\n------------------
 ---\nWide-field Phase-Contrast Micro-CT and Computational Topology for the
  3D Exploration of Prostate Cancer and other Soft Tissue Tumors\n\nThe dia
 gnosis and grading of cancer rely on the examination of abnormal tissue an
 d the morphology of the cells within. For example, the clinical evaluation
  of prostate cancer requires the assessment of glandular and cellular morp
 hology from histopathology images. However, prostate cancer patients su...
 \n\n\nAndrew Sugarman, Daniel Vanselow, and Joshua Warrick (Pennsylvania S
 tate University); Dilworth Parkinson (Lawrence Berkeley National Laborator
 y (LBNL)); Patrick La Riviere (University of Chicago); and Justin Silverma
 n and Keith Cheng (Pennsylvania State University)\n---------------------\n
 Closing Remarks\n\nEric Stahlberg (Frederick National Laboratory for Cance
 r Research)\n---------------------\nGeneralized Generative Molecular Desig
 n: An open source, Modular Tool for De Novo Drug Design\n\nThe chemical sp
 ace of small molecules is vast, making de novo drug design challenging. Tr
 aditional methods are slow and costly. While AI advancements have improved
  this process, we still face limitations in exploring the larger chemical 
 space. In oncological drug discovery, various factors such as s...\n\n\nEl
 i Brignac (University of Delaware) and Sean Black (Frederick National Labo
 ratory for Cancer Research)\n---------------------\nWelcome\n\nEric Stahlb
 erg (Frederick National Laboratory for Cancer Research)\n-----------------
 ----\nPanel: Emerging Leaders\n\nThe discussion will focus on innovation, 
 collaboration, and the critical role of future leaders in driving breakthr
 oughs at the intersection of AI and healthcare.\n\n\nKaylin Carey (Morehou
 se School of Medicine), Eli Brignac (University of Delaware), Sean Black a
 nd Justin Overhulse (Frederick National Laboratory for Cancer Research), a
 nd Joel Duah (Bowie State University)\n---------------------\nAnti-Cancer 
 Drug Response Prediction on Patient-Derived Xenografts: Learning from Extr
 emely Limited Data\n\nCancer is a family of complex genetic disorders char
 acterized by the accumulation of genetic and epigenetic alterations that d
 rive uncontrolled cell growth and metastasis. It is becoming a leading cau
 se of death worldwide, accounting for 10 million deaths in 2020. In 2024, 
 NIH projects that roughly ...\n\n\nOleksandr Narykov, Yitan Zhu, and Thoma
 s Brettin (Argonne National Laboratory (ANL)); Yvonne Evrard (Frederick Na
 tional Laboratory for Cancer Research); Priyanka Vasanthakumari, Alexander
  Partin, and Maulik Shukla (Argonne National Laboratory (ANL)); James Doro
 show (National Institutes of Health (NIH), National Cancer Institute (NCI)
 ); and Rick Stevens (Argonne National Laboratory (ANL), University of Chic
 ago)\n---------------------\nCross-HPO: Optimizing Neural Networks for Can
 cer Drug Response Using Hyperparameter Tuning on Multiple Pharmacogenomic 
 Datasets\n\nPredicting and comparing anti-cancer drug responses using deep
  learning models across datasets is a challenging modern problem. In this 
 study, we optimized hyperparameters in several novel neural network-based 
 models, including GraphDRP [1], IGTD [2], Paccmann [3], PathDSP [4], and H
 iDRA [5], and a ...\n\n\nRajeev Jain, Justin M. Wozniak, Alexander Partin,
  Andreas Wilke, Yitan Zhu, Priyanka Vasanthakumari, Oleksandr Narykov, Jam
 ie Overbeek, and Rylie Weaver (Argonne National Laboratory (ANL)); Chen Wa
 ng and Yuanhang Liu (Mayo Clinic); Ryan Weil (National Institutes of Healt
 h (NIH)); and Thomas Brettin and Rick Stevens (Argonne National Laboratory
  (ANL))\n---------------------\nReproducible Radiology and Pathology Imagi
 ng Analysis using a Standard Model Inferencing Architecture\n\nIt can be d
 ifficult for medical imaging researchers to use machine learning (ML) mode
 ls developed by others because usage conventions vary greatly between deve
 lopers. Additionally, many ML training pipelines do not support the DICOM 
 standard for medical imagery.   These factors can make it difficult...\n\n
 \nCurtis Lisle (KnowledgeVis, LLC)\n---------------------\nPanel: What is 
 Healthy AI?\n\nAs a unifying theme in 2024, the special topic “What is Hea
 lthy AI?” will focus on the intersection of technology and innovation with
  cancer biology. “What is Healthy AI” embarks on the journey to craft the 
 correct components for data and algorithms to seamlessly coexist with...\n
 \n\nEric Stahlberg (Frederick National Laboratory for Cancer Research), Kr
 istin Higgins (City of Hope), Kaylin Carey (Morehouse School of Medicine),
  Rishi Kamaleswaran (Duke University School of Medicine), and Walkitria Sm
 ith (Morehouse School of Medicine)\n---------------------\nThe Hallmarks o
 f Predictive Oncology\n\nPredictive oncology can be defined as the branch 
 of precision medicine focused on improving cancer treatment outcomes by cu
 stomizing therapeutic decisions for each patient based on all available in
 formation – genetic, molecular, cellular, and clinical. The rapid evolutio
 n of machine learning h...\n\n\nAkshat Singhal (University of California S
 an Diego); Ryan Weil (Frederick National Laboratory for Cancer Research); 
 Benjamin Haibe-Kains (University of Toronto, Canada); and Trey Ideker (Uni
 versity of California San Diego)\n\nTag: Artificial Intelligence/Machine L
 earning, Biology, Education, Emerging Technologies, Medicine, Modeling and
  Simulation\n\nRegistration Category: Workshop Reg Pass\n\nSession Chairs:
  Lynn Borkon (Frederick National Laboratory for Cancer Research); Lauren L
 ewis (Frederick National Laboratory for Cancer Research); and Eric Stahlbe
 rg (MD Anderson Cancer Center, University of Texas)
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