News

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

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.

Discover how the algorithms produced in this challenge performed in detecting breast 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!

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.

A new, NCI-funded, deep learning technology performed on par with radiologists in interpreting breast cancer images. This tool could help refine diagnosis to reduce the number of unnecessary biopsies.

A new $14 million project, funded by NIH’s Bridge2AI program, is turning the traditional biomarker concept on its ear. Instead of examining genetic or similar molecular characteristics, researchers are collecting data to look for voice biomarkers that can be linked to cancer.

Collaboration, cooperation, and the need for multimodal, multiscale data were central themes in a recent article on NCI’s efforts to develop a cancer research digital twin.

A new precision medicine platform combines machine learning with sophisticated analysis to help researchers mine chromosomal alterations linked to cancer.