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

The Childhood Cancer Data Initiative (CCDI) awarded administrative supplements to eight NCI-Designated Cancer Centers, enabling research that leverages the CCDI Data Ecosystem to address scientific questions and create analytical tools for advancing childhood cancer research.

In a recent study, NCI’s Dr. Haoyu Zhang describes CT-SLEB—a powerful and computationally scalable method for generating more precise polygenic risk scores across a range of ancestral groups (including Latino, African American, East Asian, and South Asian).

Learn about this model that predicts survival and disease outcome in patients with head and neck squamous cell carcinoma.

CAFCW23 brings together data and computational scientists, cancer biologists, and developers interested in accelerating the progress of computationally and data-driven cancer research and clinical applications.

Discover how SEER data and statistical models can help personalize oral cancer treatments.

NCI-funded researchers collaborated with scientists from Denmark to develop a deep learning tool for predicting risk for pancreatic cancer, an aggressive cancer that’s difficult to diagnose in the early stages of disease.

NCI’s Childhood Cancer Data Initiative has a new online Hub. If you’re a researcher, doctor, or citizen scientist, you now have quick-and-easy access to a rapidly growing inventory of data, tools, and resources on childhood cancer data.

Explore these two articles, both published by members of the NCI and Department of Energy Collaboration!

NCI-funded researchers used a machine learning approach to identify patients who were most likely to benefit, or have adverse effects, from cancer treatment late in life.

NCI-funded researchers collaborated with scientists from NCI’s Division of Cancer Epidemiology and Genetics and Division of Cancer Prevention on a study using a deep learning model to prioritize screening for lung cancer.