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

Meet the new branch chiefs at CBIIT who will oversee informatics infrastructure and data ecosystem activities.

NCI designed the Cancer Moonshot Scholars program to advance cancer science while also diversifying the pool of NCI-funded researchers. NCI encourages investigators from diverse backgrounds, including data scientists, to apply by November 8, 2022.

The NIH Common Fund’s Bridge to Artificial Intelligence program launched this month, aiming to bridge the gap between biomedical and behavioral research and artificial intelligence.

NCI’s Small Business Innovation Research Development Center released four new contract funding topics. The topics aim to advance work in the fields of wearable technologies, digital tools, and artificial intelligence.

Are you interested in comparative oncology? NCI needs input on the future needs and directions of the Integrated Canine Data Commons. Submit a response by Thursday, December 1, 2022.

The latest update to the Childhood Cancer Data Catalog includes new and updated data, improved accessibility, as well as enhanced site search tools.

Discover how researchers are using NIH/NCI genomics and proteomics data to gain insight into chemotherapy resistance in triple negative breast cancer.

Learn how NCI CT (Computed Tomography) imaging data sets enable the use of artificial intelligence in planning treatment for non-small cell lung cancer.

A new job opportunity is available supporting research and training program officers in developing bioinformatics and computational approaches, joining biology with computer science, engineering, mathematics, statistics, data science, and physics.

An NCI training grant and resources such as the NCI Cancer Research Data Commons’ Genomic Data Commons, in part, made it possible for this study to use multimodal deep learning. This model allowed researchers to examine pathology whole slide images and molecular profile data from 14 cancer types to enable more accurate patient outcome predictions.