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UID:submissions.supercomputing.org_SC23_sess430@linklings.com
SUMMARY:Ninth Computational Approaches for Cancer Workshop (CAFCW23)
DESCRIPTION:Environmental Factors and Lung Cancer:  A Predictive Spatial A
 pproach\n\nLung cancer has witnessed a substantial increase in prevalence 
 over the past few decades. While studies have established that the environ
 ment is the primary cause of most lung cancer cases, the development of lu
 ng cancer may be the result of the combined impact of multiple environment
 al factors.\n\nOu...\n\n\nWenhuan Tan, Xiange Wang, and Silvia Crivelli (L
 awrence Berkeley National Laboratory (LBNL)) and Xinlian Liu (National Ins
 titutes of Health (NIH), National Institute of Child Health and Human Deve
 lopment; Lawrence Berkeley National Laboratory (LBNL))\n------------------
 ---\nOptimized Patient-Specific Catheter Placement for Convection-Enhanced
  Nanoparticle Delivery in Recurrent Glioblastoma\n\nIntroduction: Glioblas
 toma multiforme (GBM) is the most common and deadliest of all primary brai
 n cancers. One promising treatment strategy for patients with recurrent GB
 M is convection-enhanced delivery (CED) of Rhenium-186 (186Re)-nanoliposom
 es (RNL) to provide delivery of large, localized doses o...\n\n\nChengyue 
 Wu (University of Texas, Oden Institute)\n---------------------\nOvercomin
 g the Challenges to Democratizing Precision Medicine:  HPC Infrastructure,
  Health Equity Training Sets, Training a Diverse Workforce, and Mitigating
  Fears\n\nThis presentation will discuss the challenges and opportunities 
 of democratizing precision medicine to make accessible to everyone, includ
 ing underrepresented populations. The talk will describe high performance 
 computer infrastructure needed by community health learning systems, healt
 h equity train...\n\n\nJames Lillard (Morehouse School of Medicine)\n-----
 ----------------\nPanel:  Diversity, Equity, and Inclusion – From Data to 
 Workforce\n\nSally Ellingson (University of Kentucky)\n-------------------
 --\nScalable Lead Prediction with Transformers Using HPC Resources\n\nA pr
 omising direction in cancer drug discovery is high-throughput screening of
  extensive compound datasets to identify advantageous properties, includin
 g their ability to interact with relevant biomolecules such as proteins. H
 owever, traditional structural approaches for assessing binding affinity, 
 ...\n\n\nArchit Vasan, Rick Stevens, Arvind Ramanathan, and Venkatram Vish
 wanath (Argonne National Laboratory (ANL))\n---------------------\nCAFCW23
  – Afternoon Break\n---------------------\nAI/ML-Derived Whole-Genome Pred
 ictor Prospectively and Clinically Predicts Survival and Response to Treat
 ment in Brain Cancer\n\nCancer is complex, with contributing factors distr
 ibuted across the entire genome affecting every aspect of the disease. But
  typical artificial intelligence and machine learning (AI/ML) would requir
 e 3B-patient training sets to generate predictive models from the whole 3B
 -nucleotide genome. As a res...\n\n\nSri Priya Ponnapalli (University of U
 tah); Penelope Miron and Kristy L. S. Miskimen (Case Western Reserve Unive
 rsity School of Medicine); Kristin A. Waite (National Cancer Institute (NC
 I)); Nadiya Sosonkina (HudsonAlpha Clinical Services Lab LLC); Sara E. Cop
 pens and Anthony C. Bryan (The Abigail Wexner Research Institute at Nation
 wide Children’s Hospital); Estevan P. Kiernan (Illumina, Inc.); Huanming Y
 ang (Beijing Genomics Institute (BGI) -Shenzhen); Jay Bowen (The Abigail W
 exner Research Institute at Nationwide Children’s Hospital); Ghunwa A. Nak
 ouzi (HudsonAlpha Clinical Services Lab LLC); Jill S. Barnholtz-Sloan (Nat
 ional Cancer Institute (NCI)); Andrew E. Sloan (Piedmont Healthcare, Case 
 Western Reserve University School of Medicine); Tiffany R. Hodges (Case We
 stern Reserve University School of Medicine); and Orly Alter (University o
 f Utah)\n---------------------\nConstructing a Large-Scale Biomedical Know
 ledge Graph and Its Applications in Drug Discovery\n\nIn the past few deca
 des, the biomedical research community has acquired a wealth of knowledge,
  much of which is stored in scientific literature as unstructured text. Co
 nverting this text into structured form is crucial for developing new meth
 odologies and applications that can fully utilize this kno...\n\n\nJinfeng
  Zhang (Florida State University, Insilicom LLC)\n---------------------\nE
 ntropy-Based Regularization on Deep Learning Models for Anti-Cancer Drug R
 esponse Prediction\n\nThis work studies a particular setting for regressio
 n problems – tasks with complex combinatorial data space where samples can
  be divided into distinct groups. Anti-cancer drug response prediction is 
 a perfect example of this setting, in which each sample includes cancer bi
 ological features an...\n\n\nOleksandr Narykov, Yitan Zhu, and Thomas Bret
 tin (Argonne National Laboratory (ANL), Data Science and Learning Division
 ); Yvonne Evrard (Frederick National Laboratory for Cancer Research); Alex
 ander Partin, Maulik Shukla, and Priyanka Vasanthakumari (Argonne National
  Laboratory (ANL), Data Science and Learning Division); James Doroshow (Na
 tional Cancer Institute (NCI)); and Rick Stevens (Argonne National Laborat
 ory (ANL))\n---------------------\nCAFCW23 – Announcements\n--------------
 -------\nAutomated Whole-Body Tumor Segmentation and Prognosis of Cancer o
 n PET/CT\n\nBackground: Cancer is the second leading cause of death in the
  United States [1]. Automatic characterization of malignant disease is an 
 important clinical need to facilitate early detection and treatment of can
 cer [2]. Advances in machine learning (ML) and deep learning (DL) have sho
 wn significant p...\n\n\nKevin H. Leung (Johns Hopkins University)\n\nTag:
  Applications, State of the Practice\n\nRegistration Category: Workshop Re
 g Pass\n\nSession Chairs: Lynn Borkon (Frederick National Laboratory for C
 ancer Research); Sally Ellingson (University of Kentucky); Sean Hanlon (Na
 tional Institutes of Health (NIH), National Cancer Institute (NCI)); Patri
 cia Kovatch (Icahn School of Medicine at Mount Sinai); and Eric Stahlberg 
 (MD Anderson Cancer Center, University of Texas)
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