Topic: Data Science

New computational opportunities and challenges have emerged within the cancer research and clinical application fields, as the size, source and complexity of cancer datasets have grown. Simultaneously, advances in computational capabilities, with exceptional growth in AI and deep learning, are reaching unprecedented scales.

This Sixth Computational Approaches for Cancer Workshop 2020 (CAFCW20) will bring together a wide-range of individuals including clinicians, cancer biologists, mathematicians, data scientists, computational scientists, engineers, developers, thought leaders and others with an interest in advancing the use of computation to better understand, diagnose, treat and prevent cancer. As an interdisciplinary workshop, the sharing of insight and challenges fosters collaborations and future innovations accelerating progress in computationally and data-driven cancer research and clinical applications.

High-performance computing (HPC) has been and will continue to be a key component of cancer research. Industry, academic and government interest is demonstrably high with ongoing commitments, new announcements, advances and new opportunities involving cancer and computing. One need only review recommendations provided by the National Cancer Moonshot Blue Ribbon Panel to confirm the increasingly visible and critical role computing and HPC in particular will play in accelerating cancer research objectives. As HPC-related efforts from projects funded through the 21st Century Cures Act begin to mature, the workshop will provide an ongoing avenue for new computational approaches involving HPC at all scales to be shared with the growing community.

The Computational Approaches for Cancer workshop series originated in early 2015, following observations that the topic of cancer was already pervasive at the SC conference, yet no venue at SC existed to bring the key community together. The response has been favorable for the first five workshops with over 80 participants in each of the first two years, expanding to an estimated 150 attendees at SC17 and at room capacity in SC18 and SC19. Enthusiasm for the workshop continues to grow with many ideas and challenges shared, collaborations envisioned and needs identified. The successful call for papers in SC17 resulted in proceedings published for Open Access in BMC Bioinformatics, a growing number of submissions in SC18 and a record number of submissions in SC19. At SC19, the best paper award was given to a team who has progressively presented their work at the series of SC Computational Approaches for Cancer workshops.

Questions? Contact cafcw@nih.gov

Register

RStudio will be planning quarterly webinars to support the HHS Data Science and Statistical Programming communities. The first installment will be focused on Creating Reproducible Data Science. Please join Alex Gold as he shares best practices and demonstrates project-based workflow, version control with git, creating templates and packages, and reproducing data science environments.

Alex is a Solutions Engineer at RStudio, where he helps organizations succeed using R and RStudio products. Before coming to RStudio, Alex was a data scientist and worked on economic policy research, political campaigns, and federal consulting.

For more information about RStudio in Life Sciences: https://rstudio.com/solutions/pharma/

Learn more about RStudio’s recommended professional data science solution for every team: https://rstudio.com/products/team/

Contact our team with any questions: life-sciences-healthcare@rstudio.com

Presenter:
Alex Gold