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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.

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

With help from NCI’s Small Business Innovation Research (SBIR) program, researchers are refining a biophysical simulation technology using computational models for personalized cancer care.

Explore the newly released pediatric data set that includes information on DNA/RNA sequencing, methylation arrays, and deidentified clinical reports.

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

Dr. Peng Jiang of NCI’s Center for Cancer Research Cancer Data Science Lab and his postdocs have developed an open-source computational tool called the tumor-resilient T cell (Tres) model. Tres analyzes gene activity in T cells to assess how those cells are likely to fare in an immunosuppressive environment.

Help the FDA’s National Center for Toxicological Research and the precisionFDA optimize data processing pipelines for identifying indels (i.e., insertions/deletions in a genome) in oncopanel sequencing datasets by participating in a sequencing data challenge. Pre-register for the “Indel Calling from Oncopanel Sequencing Data Challenge” before May 2, 2022! Selected participants will be publicly recognized and invited to contribute to a scientific manuscript and a “Top Performer Webinar” that will be open to the public.

IMPROVE focuses on improving deep learning models to predict the efficacy of cancer treatments. The research community, including data scientists and informaticists, is asked to respond to an RFI for creating protocols to evaluate model performance by April 15, 2022, at 5:00 p.m. ET. Additionally, they are encouraged to respond to an RFP for improving model comparison by May 9, 2022.

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.

NCI DATA Scholar Dr. Jay G. Ronquillo recently published a study using NIH “All of Us” data and NCI’s Cancer Research Data Commons to better understand pharmacogenomic prescribing and testing patterns across the United States.