News

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

Are you skilled in conceptualizing, developing, and managing projects in clinical research informatics? Apply today to work as a clinical research informatics specialist at NCI’s CBIIT.

Do you conduct research on statistical and analytical methods, cancer survivorship, digital health, and/or data science tools and methods? Apply for an R01 grant from NCI’s Division of Cancer Control and Population Sciences by June 5 or October 5, 2024.

What if you could predict how a chemotherapy drug would work—in terms of sensitivity and side-effects—before you ever use it? NCI-funded researchers are using machine learning models to better understand a key mechanism underlying cancer, giving us new ways to predict responses to common chemotherapy drugs.

A new algorithm is showing promising results in predicting lung cancer. See how proteins in your blood may someday help determine your risk for lung cancer.

Do you work with digital twin data models? If so, consider applying by March 21, 2024, for research funding!

The ways that cells interact with neighboring cells and their environments can either regulate or promote how tumors grow and spread. See how a new tool is helping us better understand these important “neighborhood” interactions.

Interested in digital twin technology? Read this report for a “reality check” on what is currently known about the technology and what’s still missing.

NCI-funded researchers are using a machine learning approach to predict cancer outcomes based on epigenetic data, which take into account both environmental and genetic influences.

A new deep learning model, called CXR-Lung-Risk, proved useful in identifying people at risk for dying from lung cancer or other lung disease, based on a single X-ray image.

Are you interested in developing a machine learning model to predict disease? Get advice from researchers who built a successful model for predicting breast cancer outcomes.