News

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

Are you planning to attend this year’s American Association for Cancer Research (AACR) Annual Meeting? Make sure to add these NCI-affiliated data science activities to your schedule!

Attending the AMIA 2024 Informatics Summit? Don't miss these NCI CBIIT-affiliated data science sessions!

Learn more about the team that won the 2023 DataWorks! Challenge Grand Prize. The winners crowdsourced data gathering and used a common, interoperable platform to understand the impacts of COVID-19 on people with a current or past history of cancer.

Are you having trouble prioritizing which genetic variants to study in your cancer research? There’s a new platform, called FORGEdb, that can help you pinpoint promising variants and target genes.

Looking to become more familiar with artificial intelligence and GitHub for your data science needs? Check out these two courses from the Informatics Technology for Cancer Research Training Network.

Interested in blending clinical and genetic data? Upgrades in cBioPortal can help you work with these very different data types to better understand cancer and how it progresses over time.

What do you think of the updated Strategic Plan for Data Science from NIH? Respond to this Request for Information by March 15!

Participate in the upcoming challenge to assess NCI Cancer Research Data Commons' compatibility with AI/ML technologies.

Do you conduct research on statistical and analytical methods, cancer survivorship, digital health, and/or data science tools and methods? Apply for an R01 grant from NCI’s Division of Cancer Control and Population Sciences by June 5 or October 5, 2024.

What if you could predict how a chemotherapy drug would work—in terms of sensitivity and side-effects—before you ever use it? NCI-funded researchers are using machine learning models to better understand a key mechanism underlying cancer, giving us new ways to predict responses to common chemotherapy drugs.