Cancer Data Science Pulse

The Cancer Data Science Pulse blog provides insights on trends, policies, initiatives, and innovation in the data science and cancer research communities from professionals dedicated to building a national cancer data ecosystem that enables new discoveries and reduces the burden of cancer.

This blog post, the fifth, concludes our series that discusses the principles underlying the collaborative project "Joint Design of Advanced Computing Solutions for Cancer (JDACS4C)."

NCI continues to identify and link external data sources with SEER data to enable the expansion of longitudinal data to form patient trajectories and to support modeling efforts. To inform the incorporation of those additional sources, NCI compiled an extensive breast cancer recurrence data dictionary to identify recurrence-related data elements across multiple sources, including pathology, radiology, pharmacy, biomarkers, procedures, comorbidities, patient-generated information, and radiation oncology.

For this interview, the Center for Biomedical Informatics and Information Technology Communications Team interviewed Dr. Robert L. Grossman of the University of Chicago Center for Data Intensive Science to discuss the Data Commons Framework, a component of the NCI Cancer Research Data Commons.

This is the third in a series of posts that discuss the principles underlying the three-year collaborative program "Joint Design of Advanced Computing Solutions for Cancer (JDACS4C)."

This is the second of a series of posts that discuss the principles underlying the three-year collaborative program “Joint Design of Advanced Computing Solutions for Cancer (JDACS4C).”

This is the first of a series of posts that discuss the pilot collaborative program “Joint Design of Advanced Computing Solutions for Cancer (JDACS4C)” being pursued by the National Cancer Institute (NCI) and the Department of Energy (DOE).

In 2016, a Blue Ribbon Panel (BRP) was established, as part of the Beau Biden Cancer Moonshot, to make key recommendations that would support the Moonshot goals of accelerating progress in cancer research and breaking down barriers to developing new treatments.

In the past year, the use of Artificial Intelligence (AI) in radiology, also called "radiomics," has been getting a lot of attention, mainly because of the progress Deep Learning (DL) has made from a sub-human performance to performance that is similar, or in some cases superior, to that of humans.

Now is the time for researchers across domains to ideate together, share data, and maximize the utility of those data. This is "the urgency of now" according to former Vice President Joe Biden, who delivered the keynote address to those in attendance at the September 2017 Human Proteome Organization (HUPO) Annual World Congress.

The data science community is awash with "FAIRness." In the past few years, there has been an emerging consensus that scientific data should be archived in open repositories, and that the data should be Findable, Accessible, Interoperable, and Reusable.