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

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.

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.

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

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.

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.

Check out this updated Notice of Special Interest if you’re interested in supplemental funds for activities that will make NIH-supported data usable for artificial intelligence and machine learning analytics!

Discover how the algorithms produced in this challenge performed in detecting breast cancer.

NCI-funded researchers combined long-term, patient-outcome data with pathology slides from people with colorectal cancer to develop a machine learning tool, called QuantCRC. Using QuantCRC, researchers could predict if a patient’s cancer would recur based on analysis of a single hematoxylin and eosin stained slide of the tumor.