News

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

Explore these two articles, both published by members of the NCI and Department of Energy Collaboration!

Researchers create a theoretical model to enable simulations based on Dynamic Density Functional Theory. This new model can help them understand the behavior of cancer-causing proteins better.

Discover how the algorithms produced in this challenge performed in detecting breast cancer.

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.