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DTSTART;TZID=America/Chicago:20211114T090000
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UID:submissions.supercomputing.org_SC21_sess434@linklings.com
SUMMARY:CAFCW21: Computational Approaches for Cancer Workshop 2021
DESCRIPTION:Workshop\n\nCAFCW21:  Morning Break (10:00)\n\n\n\n-----------
 ----------\nFrom Regulatory Networks to Microenvironments: Multiscale Mult
 icellular Cancer Modeling and Simulation with CompuCell3D\n\nSego\n\nModel
 ing the interplay of subcellular, intercellular and environmental factors 
 is critical to producing meaningful, predictive simulations of cancer prog
 ression in vivo and testing biological hypotheses with virtual tissues. Co
 mpuCell3D is an open-source, cross-platform simulation environment that p.
 ..\n\n---------------------\nTopological Interpretation of Deep-Learning M
 odels\n\nSpannaus, Gao, Schaefferkoetter, Penberthy, Doherty...\n\nDevelop
 ing trust in the predictions made from an AI-based algorithm is a tantamou
 nt concern,  especially in systems such as threat detection or medical dia
 gnosis where outcomes may have tragic consequences. This work presents a t
 opologically informed methodology for inferring prominent features in a...
 \n\n---------------------\nCAFCW21:  Workforce Development and Inclusion P
 anel\n\nEllingson, Akpa, Berny-Lang, Zahir, Lukan\n\n---------------------
 \nElectronic Health Records (EHR) Significantly Under-Capture Patient Co-M
 orbidity\n\nDilling, Howard, Ansley\n\nBackground:\nThe concept of a digit
 al twin in healthcare is predicated upon mimicking, as closely as possible
 , the clinical state of the patient. However, EHR implementations might su
 ffer in data quality, as they are incumbent upon accurate/complete data en
 try (typically) into discrete data fields. U...\n\n---------------------\n
 Predicting Tumor Time to Recurrence from Free-Text Notes\n\nNagaraj\n\nEle
 ctronic medical records contain a significant amount of unstructured patie
 nt information from free text, but crucial information can be difficult to
  find within lengthy notes. Thus, we develop an automated tool that can de
 tect mentions of tumor recurrence and progression in clinical, radiology, 
 a...\n\n---------------------\nFeatured Speaker:  Graham Johnson – Simular
 ium Viewer: An Online Tool for Democratizing the Analysis of Spatiotempora
 l Biological Models\n\nJohnson\n\nThe Allen Institute for Cell Science aim
 s to understand the principles by which human induced pluripotent stem cel
 ls organize and change throughout differentiation and disease. We have rec
 ently begun implementing a plan to adjust our computational infrastructure
  to maximize the impact of both our sc...\n\n---------------------\nCAFCW2
 1:  Afternoon Break (3-3:30)\n\n\n\n---------------------\nProbing Decisio
 n Boundaries in Cancer Data Using Noise Injection and Counterfactual Analy
 sis\n\nJain, Shah, Mohd-Yusof, Wozniak, Brettin...\n\nAdvanced analyses an
 d computations based on gene expressions are prone to errors as they depen
 d on experimental design, chemical operations/measurements, and data analy
 sis. The assembly and aggregation of such data for creating deep neural ne
 twork models may further influence the accuracy of these a...\n\n---------
 ------------\nImage-Informed Mathematical Modeling to Predict Patient-Spec
 ific Treatment Response to Neoadjuvant Systemic Therapy in Triple Negative
  Breast Cancer\n\nWu\n\nPatients with locally advanced, triple-negative br
 east cancer (TNBC) typically receive neoadjuvant therapy (NAT) to downstag
 e the tumor and for improved surgical outcomes. A critical, unmet need is 
 a method to accurately predict an individual patient’s response to NAT, th
 ereby allowing for the oppor...\n\n---------------------\nCAFCW21 Lunch Br
 eak (12:30-2)\n\n\n\n---------------------\nCAFCW21: Computational Approac
 hes for Cancer Workshop 2021\n\nStahlberg, Hanlon, Ellingson, Kovatch, Bor
 kon...\n\nNew computational opportunities and challenges have emerged with
 in the cancer research and clinical application areas as the size, source 
 and complexity of cancer datasets have grown.  Simultaneously, advances in
  computational capabilities, with exceptional growth in AI and deep learni
 ng, are reachi...\n\n---------------------\nOncolomics: Digital Twins and 
 Digital Triplets in Cancer Care\n\nTalukder, Haas\n\nTo address the comple
 x challenges in cancer care we integrated two digital twins namely, (A) Di
 gital twin of oncologists' mind, and (B) Digital twin of the patients' phy
 sical state. The oncologist's twin is realized through the semantic integr
 ation of (1) NCI Thesaurus (NCIt), (2) Gene Ontology (GO)...\n\n----------
 -----------\nCAFCW21: Digital Twin Panel\n\nGreenspan, McCoy, Macklin, Sye
 da-Mahmood, Shahriyari\n\n---------------------\nCAFCW21: Wrap Up\n\n\n\n-
 --------------------\nAn Integrated Simulation-HPC-Learning Approach to Cr
 eate Cancer Patient Templates for Digital Twins\n\nLima da Rocha\n\nCancer
  patient digital twins (CPDTs) are personalized simulation models that can
  forecast individuals’ prognosis under a variety of treatment options. To 
 successfully launch CPDTs, we must combine mechanistic modeling, artificia
 l intelligence (AI), and high performance computing (HPC) into a platfor..
 .\n\n---------------------\nGenomicSuperSignature: Interpretation of RNA-S
 eq Experiments through Robust, Efficient Comparison to Public Databases\n\
 nOh, Waldron, Davis\n\nMillions of transcriptomic profiles have been depos
 ited in public archives, yet remain underused for the interpretation of ne
 w experiments. Existing methods for leveraging these public resources have
  focused on the reanalysis of existing data or analysis of new datasets in
 dependently. We present a n...\n\n---------------------\nCAFCW21:  Welcome
  and Introductions\n\nStahlberg\n\n---------------------\nLeveraging High-
 Performance Computing and Quantitative Imaging for Personalized Spatial-Te
 mporal Forecasts of High-Grade Glioma Treatment Response\n\nHormuth, Farha
 t, Curl, Yankeelov, Chung\n\nMaximal safe resection followed by combinatio
 n radiotherapy and chemotherapy is the standard treatment approach for pat
 ients with high-grade gliomas to target residual and infiltrative tumor. R
 esponse to therapy depends on the ability to target the tumor and on the t
 reatment sensitivity influenced b...\n\n\nTag: Applications, Computational
  Science, Education and Training and Outreach, HPC Community Collaboration
 , HPC Training and Education, Machine Learning and Artificial Intelligence
 , Performance, Workforce\n\nRegistration Category: Workshop Reg Pass
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