About our Workshop

As the drive towards precision medicine has accelerated, the opportunities and challenges in using computational approaches in cancer research and clinical application are rapidly growing. The rapid rise of deep learning as an enabling technology and its potential are reshaping the way computation is being applied across scales scales of computing, across time and across spatial scales. With recent legislation in the form of the Twenty-first Century Cures Act as well as efforts of the Beau Biden Cancer Moonshot all underscore the importance of a workshop that brings together experts and insights across the spectrum of computational approaches for cancer.

In the workshop, we bring together the computational community exploring and using high-performance computing, analytics, predictive modeling, and large datasets in cancer research and clinical applications. The workshop is inherently inter-disciplinary, with the common interest in cancer and computation the unifying theme. As such, the workshop provides rich opportunities for attendees to learn about future directions, current applications and challenges and build collaborations. Maintaining a perspective of accelerating scientific insights and translation of insights to clinical application for improved patient outcomes, the workshop brings together many interests from across the technology, cancer research and clinical domains.

Workshop Goals

  • Bring together individuals employing computation in studying, diagnosing, treating, or preventing cancer
  • Attract individuals with HPC and computational skills in technical areas that would contribute to the understanding, diagnostics, treatment and/or prevention of cancer
  • Provide an overview of current applications of computational approaches at several levels in cancer research and clinical application
  • Educate attendees on the ways that computation is used in several areas of cancer research and clinical applications (imaging, genomics, analytics, molecular modeling, complex systems modeling, drug discovery, pathology, etc.)
  • Discuss and bring forward issues and challenges facing greater utilization of computation at all levels in cancer research and clinical applications (e.g. reproducibility, portability, auditability, standards, regulatory compliance, etc.)
  • Explore future opportunities where employing HPC from terascale to exascale will help advance cancer research and clinical communities

Target Audience

The target audience for the workshop is those with shared interests in computational approaches for cancer. The workshop is again expected to attract those developers, researchers, and vendors with technologies and solutions believed to hold potential to address problems in cancer and seeking potential collaborators to work with. The workshop will also attract cancer investigators, clinicians and others who have recognized the need for HPC solutions, seeking an overview of potential solutions and potential collaborators. The third segment of the target audience are those individuals seeking career opportunities in cancer research and/or clinical applications, where the workshop will highlight forward looking directions in these areas.

Workshop Program Overview

This year’s program will continue to provide the broad-base community interaction critical to the interdisciplinary advances needed to accelerate cancer research and clinical applications. With a rapidly evolving field, the session will survey exiting new developments in computational approaches for cancer, selected from emerging topics of interest.

In addition, this year’s program will include a special session on Machine Learning Applied to Cancer – highlighting advances and paper submissions from the HPC and cancer research community.

CAFCW 2017 Special Session Topic:
Machine Learning Applied to Cancer

The CAFCW workshop annually identifies a special workshop focus of significant interest to the community, bringing a special emphasis to the workshop for the year. The use of machine learning in multiple contexts (AI, cognitive learning, deep learning, etc.) has dramatically accelerated in the cancer research and clinical space. This has led to several innovations and rapid development of new techniques, while highlighting key challenges to overcome in order to more fully utilize these technologies.

Workshop Program

8:30 AM, Workshop Introduction

Workshop Organizers

8:40 AM, Keynote Presentation

Dr. Shannon Hughes, NCI, Division of Cancer Biology

9:15 AM, Paper Session 1

  • High-throughput cancer hypothesis testing with an integrated PhyisCell-EMEWS workflow, J. Ozik, N. Collier, J. Wozniak, C. Macal, C. Cockrell, S. Friedman, A. Ghaffarizadeh, R. Heiland, G. An, and P. Macklin
  • Sparse Coding of Pathology Slides, S. Moudgalya, W. Fischer and G. Kenyon
  • A Workflow Framework for Machine Learning Applied to Cancer Research, J. Wozniak, R. Jain, P. Balaprakash, J. Ozik, N. Collier, J. Bauer, F. Xia, T. Brettin, R. Stevens, K. Mohi-Yusof, C. Cardona, B. Van Essen, and M. Baughman

10:15 AM, Collaboration Frontiers - NCI-DOE Collaborations Update

10:45 AM, Paper Session 2

  • Boosting Curative Surgery Success Rates with FPGAs, A. Sanaullah, M. Herbordt, C. Yang, Y. Alexeev, and K. Yoshil
  • High-throughput Binding Affinity Calculations at Extreme Scales, S. Jha, M. Turilli, and D. Wright
  • Predicting Tumor Cell Line Response to Drug Pairs with Deep Learning, F. Xia, M. Shukla, T. Brettin, C. Garcia-Cardona, J. Cohn, J. Allen, S. Maslov, Y. Evrard, S. Holbeck, J. Doroshow, E. Stahlberg, and R. Stevens

11:30 AM, Machine Learning, AI, and Cancer Panel Discussion

Patricia Kovatch, Moderator

11:55 AM, End of Workshop Wrap-Up

Workshop Organizers


2017 Organizing Committee

  • Thomas Barr - The Research Institute at Nationwide Childrens Hospital
  • Patricia Kovatch - Icahn School of Medicine at Mount Sinai
  • Eric Stahlberg - Frederick National Laboratory for Cancer Research

2017 Program Committee

  • Sunita Chandrasakaran - University of Delaware
  • Claudine Conway - Intel
  • Sally Ellingson - University of Kentucky
  • Heiko Enderling - Moffitt Cancer Center
  • Amy Gryshuk - Lawrence Livermore National Laboratory
  • Florence Hudson - Internet2
  • Abdul Al Halabi - NVIDIA
  • William Richards - Brigham and Womens Hospital, Harvard Medical School
  • Ilya Shmulevich - Institute for Systems Biology
  • Thomas Steinke - Zuse Institute Berlin

We look forward to welcoming you to Denver!