News

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

Looking to enhance your institution’s capacity for data science tools and methodologies? Do you currently hold an NIH grant? Apply for an administrative supplement and get additional funding for that grant!

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.

Are you using cancer data or other emerging technologies to address health disparities? Maybe you’re a researcher from a minority or underrepresented group working in cancer data science? If so, NIH has new funding opportunities to help you in your work and make a difference in addressing inequities in public health and biomedical research.

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.

Do you work in the health technology field? The Office of the National Coordinator for Health Information Technology wants your input on a new rule related to electronic health data.

Cancer researchers can help NCI enhance cancer metabolomics research by applying for funding to develop innovative technologies and informatics tools.

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

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.

Researchers create a theoretical model to enable simulations based on Dynamic Density Functional Theory. This new model can help them understand the behavior of cancer-causing proteins better.

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.