News

Keep up with the latest news from the NCI Center for Biomedical Informatics and Information Technology (CBIIT) and the data science communities.

NCI’s Surveillance, Epidemiology, and End Results (SEER) Program is marking 50 years of cancer surveillance research and launching three new initiatives that are likely to be of particular interest to the data science field. These efforts include a Virtual Tissue Repository, a Virtual Pooled Registry, and a partnership with the Department of Energy to develop application programming interfaces.

The PLCO Atlas allows investigators to browse and access germline genetic association data from the PLCO Screening Study via the Genome-Wide Association Study Explorer.

Planning your itinerary for this year’s American Association for Cancer Research (AACR) Annual Meeting? Want to make sure you catch the NCI-affiliated data science activities? We’ve put together a helpful reference page for you!

NIH wants to hear from you about the NIH Public Access Plan, which shows how to accelerate access to federally supported scientific data and research.

A new, NCI-funded, deep learning technology performed on par with radiologists in interpreting breast cancer images. This tool could help refine diagnosis to reduce the number of unnecessary biopsies.

Researchers seeking potential targets for treating childhood cancers now have an even better tool for the job. Check out the latest enhancements to the NCI Childhood Cancer Data Initiative’s (CCDI’s) Molecular Targets Platform.

Funding can be used to support data preparation for inclusion in the National Institute of Allergy and Infectious Diseases (NIAID) Data Ecosystem.

CCDI additions include molecular characterization data from childhood cancer patients of racial and ethnic diversity.

NCI is soliciting feedback on the utility and future promise of multidimensional tumor atlases for the Human Tumor Atlas Network, including high-priority data types and challenges/opportunities for computational modeling. Your responses are due January 7, 2023.

Data analysis of the DNA, RNA, protein, and phosphoprotein in lung adenocarcinoma cells connected molecular features of tumors with patient survivability. This study allowed researchers to better predict prognosis and treatment in lung cancer patients.