News

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 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.

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.

Submit your expression of interest to transform cancer research with help from data science tools and methodologies. “Cancer Grand Challenges,” a global initiative founded by NCI and Cancer Research UK, announced new research challenges to address how we prevent, diagnose, and treat cancer.

An NCI-funded project wants to increase diagnostic accuracy, reduce missed cancer diagnoses, and improve public health by changing the design of artificial intelligence (AI) medical systems to work with radiologists.

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.

Are you working on software tools to promote open science in cancer research? NCI has issued a Notice of Special Interest offering supplemental funding for work in this area. The application due date is May 9, 2023.

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!

Apply by April 11 for administrative supplemental funding to explore and test new opportunities for leveraging cloud-based solutions for NIH-funded research.

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.