News

NIH wants to hear from you about the NIH Public Access Plan, which shows how to accelerate access to federally supported scientific data and research.

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.

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

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.

As the necessity for and availability of large data sets in cancer applications grows, so do the challenges when conducting research and clinical applications with computational solutions. Share your experience with how to address such challenges.

NCI’s Office of Cancer Clinical Proteomics Research’s new blog highlights recent findings from scientists in the Clinical Proteomic Tumor Analysis Consortium. It describes a proof-of-concept approach to identifying fraudulent data in biological data sets.

In RAS-related diseases, such as cancer, mutations in the RAS genes or their regulators render RAS proteins persistently active. Investigating RAS activation events is challenging when using conventional techniques. An unprecedented multiscale platform is using machine learning to change that.

A few of NCI’s Division of Cancer Biology grantees recently released publications on topics such as machine learning and artificial intelligence. These research results hold clues to how we research and develop various cancer treatments.

Staff from CBIIT and NCI, alongside partners from NIH, FDA, and a consortium of scientists from across the world, joined forces to create reference samples and data call sets to help the cancer community further decipher cancer-related gene mutations. Their findings were recently published in Nature Biotechnology.

Dr. Jill Barnholtz-Sloan, CBIIT’s associate director of Informatics and Data Science and DCEG senior investigator, together with research colleagues, used a direct data matching approach to compare brain tumors in U.S. Veteran and non-Veteran populations. The study indicates that direct and deterministic data matching approaches have the potential to compare the distribution of tumors, treatment trajectories, and clinical outcomes of other cancers and rare diseases among these populations.