News

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

Using data from routine lung scans, NCI-supported researchers developed an AI-based tool to help predict how patients will respond to therapy.

The latest release from NCI’s Enterprise Vocabulary Services includes updates to SeroNet terminology, childhood neoplasm terminologies, as well as other standalone terminologies, ontologies, and mappings.

This new cancer screening research network will require a team that can lead data management, quality control, and reporting activities of its clinical trials and studies. To be that team, apply as soon as January 28 but no later than February 28, 2023!

Researchers seeking potential targets for treating childhood cancers now have an even better tool for the job. Check out the latest enhancements to the NCI Childhood Cancer Data Initiative’s (CCDI’s) Molecular Targets Platform.

Interested in cancer research? Apply for the Division of Cancer Biology’s Summer Undergraduate Research Program! Students will learn about solving complex problems through interdisciplinary research, bioinformatics, and mathematical modeling.

Funding can be used to support data preparation for inclusion in the National Institute of Allergy and Infectious Diseases (NIAID) Data Ecosystem.

NCI-funded researchers validated a genome-wide artificial intelligence technology that could help in early detection of hepatocellular carcinoma—the most common type of liver cancer.

CCDI additions include molecular characterization data from childhood cancer patients of racial and ethnic diversity.

NCI is soliciting feedback on the utility and future promise of multidimensional tumor atlases for the Human Tumor Atlas Network, including high-priority data types and challenges/opportunities for computational modeling. Your responses are due January 7, 2023.

Data analysis of the DNA, RNA, protein, and phosphoprotein in lung adenocarcinoma cells connected molecular features of tumors with patient survivability. This study allowed researchers to better predict prognosis and treatment in lung cancer patients.