CAFCW 2017 - Computational Approaches for Cancer Workshop (CAFCW-2017)

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.
Call for Papers
CAFCW 2017 Special 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. Papers are sought for a workshop session emphasizing cancer applications of machine learning, identifying promising breakthroughs, new resources, data challenges, and future needs to further the utilization of machine learning in cancer applications.
CAFCW17 Broad Topic Call: Computational Approaches for Cancer
In order to encourage broad participation, the workshop maintains an open call for all interests to submit papers for consideration to present at the workshop where computation or computational technologies has been employed effectively in cancer research or clinical application. Lists of potential topics are provided below, including both potential HPC technologies used in cancer applications, and cancer applications that may use HPC technologies. With a rapidly evolving field, authors are also encouraged to identify areas not listed.
Broad topic areas for the workshop may include but are certainly not limited to suggestions below.
Cancer Research and Clinical Applications
Next Generation Sequencing Analysis
Single Cell Sequencing
Proteomics, Genomics, and Metabolomics
Flow Cytometry
High-throughput Screening
Cyro-Electron Microscopy
Multi-modal Biological Imaging
Structural Biology
Biological-scale Molecular Dynamics
Predictive Oncology
Cancer Therapeutic Development
Protein-protein Interaction
Cellular Signaling
Cell-level Predictive Modeling
Cancer Imaging
Digital Pathology
Pharmacodynamic Modeling
Pharmacogenomic Modeling and Analysis
Electronic Health and Medical Records
mHealth and Health Sensor Networks
Bioinformatics
Cancer Diagnostics
Therapeutic Response
Systems Biology
Computational Approaches
High-performance Parallel Computing
Cloud Computing
Exascale and Extreme-scale Computing
Machine and/or Deep Learning
Cognitive Computing
Data Integration and Delivery
Image Processing
Pattern Recognition
Heterogeneous Computing (GPGPU, FPGA, etc.)
Programming Models
Visualization
Data Imputation
Uncertainty Quantification
Multi-scale Predictive Modeling
Integrated Systems Simulations
Complex Systems Modeling
Integration Frameworks
Computational Workflows
Information and Data Security
Automata and Finite State Machines
Novel Mathematical and Statistical Models
Data Science and Analytics
Graph and/or Network Analysis
Model Validation and Verification
Submitted papers will be reviewed and selected for presentation in the Computational Approaches for Cancer Workshop held as part of the SC 17 Workshop Program, November 17, 2017 in Denver, Colorado.
Important Dates
• Submission: August 31, 2017, at https://easychair.org/conferences/?conf=cafcw17.
• Notification of Acceptance: Notification of Acceptance: September 15, 2017.
• Workshop: November 17, 2017
Submission Guidelines
Authors are invited to submit papers in English structured as technical papers. These technical papers can be extended abstracts of 2-8 letter size pages (not including bibliography).
A bibliography should be included and use the IEEE format for conference proceedings. Submissions not conforming to these guidelines may be returned without consideration or review.
Papers will be reviewed and judged on correctness, originality, technical strength, alignment to expressed cross-disciplinary aims in the paper call, quality of presentation and interest to workshop attendees. Submitted extended abstracts may incorporate unpublished new advances, insight and/or original research findings.
Submissions received after the due date, exceeding the prescribed length, or not appropriately structured may also be returned without consideration or review.
In submitting the paper, the authors acknowledge that at least one author of an accepted submission will register for and attend the workshop.
Papers should be submitted electronically as PDF documents at
https://easychair.org/conferences/?conf=cafcw17
Proceedings: Await more details on workshop paper proceedings.
Organizing Committee
· Thomas Barr – The Research Institute at Nationwide Children’s Hospital
· Patricia Kovatch – Mount Sinai Icahn School of Medicine
· Eric Stahlberg – Frederick National Laboratory for Cancer Research
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