Deadline Extended: Submit Abstracts for the Eighth Computational Approaches for Cancer Workshop by August 16
Calling all data/computer scientists and cancer researchers: submit a written abstract to the Computational Approaches for Cancer Workshop (CAFCW22) focused on applying computational solutions to cancer research and clinical application challenges.
CAFCW22 is coordinated by the NCI-Department of Energy (DOE) partnership, aimed at accelerating cancer research using emerging computing capabilities. The CAFCW22 program committee includes CBIIT’s Emily Greenspan and Keyvan Farahani, along with Division of Cancer Treatment and Diagnosis team members, Jeff Buchsbaum, Michael Difilippantonio, and Christopher Hartshorn, and NIH’s Thomas Radman. The Center for Strategic Science Initiatives’ Sean Hanlon is a member of the organizing committee.
With the growing importance and availability of large amounts of data in cancer applications, the rapidly evolving use of machine learning, and the simultaneous incentive to find new treatments, the drive towards precision medicine has accelerated. As such, the special emphasis for this year’s CAFCW session is Accelerating New Treatments.
Abstracts are due by Tuesday, August 16, 2022, and will be considered for 15-minute presentations, with a length of up to 500 words.
Submissions will be reviewed and judged on:
- technical strength.
- integration of computational approaches and cancer research topics.
- general alignment to the workshop’s expressed cross-disciplinary aims.
- anticipated interest to workshop attendees.
Abstracts will also be considered for specific alignment to the special workshop topic: Accelerating New Treatments.
Complete submission guidelines can be found on the CAFCW22 Call for Abstracts webpage.
CAFCW22 will be held on November 13, 2022, in Dallas, TX, in conjunction with SC22, The International Conference on High Performance Computing, Networking, Storage, and Analysis. Subject matter experts will share insights and challenges to foster collaborations and future innovations to accelerate progress in computationally and data-driven cancer research and clinical applications.