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

Do you have knowledge, interest, and experience conducting childhood cancer research? NCI’s Childhood Cancer Data Initiative seeks your input to identify opportunities to generate additional research molecular characterization data to accelerate scientific discovery and improve patient care through enhanced data sharing.

You can now access images from patients who participated in Children’s Oncology Group trials and get metadata and accompanying clinical data. The gene panel sequencing data is available through the database of Genotypes and Phenotypes.

This joint NCI and ARPA-H Biomedical Data Fabric Toolbox effort will focus on developing solutions that take data science innovation in the biomedical research community to a new level. Researchers can use simple dashboards to explore data, conduct analyses, and share data across disciplines.

If you work for a small business, then this contract opportunity is for you! Support the development of commercial analytic tools for the cancer research community that integrate Cancer Research Data Commons multimodal data.

Are you using data collection technologies that would work in social networks? NCI has a notice of funding aimed at leveraging these data to develop interventions for promoting better health across the lifespan.

Learn about this model that predicts survival and disease outcome in patients with head and neck squamous cell carcinoma.

NHGRI’s Dr. Andy Baxevanis is recruiting a postdoctoral fellow to develop and apply bioinformatic approaches to large-scale genomic data sets, focusing on the evolution of specific protein families and biological pathways that have putative roles in disease causation.

If you analyze secondary data to enhance cancer risk prediction, seize this opportunity! Funding is available to those who want to create innovative data analysis techniques (that use existing data) for a better understanding of cancer risk and related outcomes.

In a recent study, NCI-funded researchers applied an artificial intelligence model to lung screening computed tomography images to assess body composition. The team used two key indicators of health—skeletal muscle and fat (adipose) tissue—to predict death from lung cancer, cardiovascular disease, and other causes.

PepQuery2 is a proteomics tool that enables rapid and targeted identification of both known and novel peptide sequences in proteomics data sets. The tool aims to provide valuable data sets for the broader research community by making public proteomics data more accessible and user-friendly.