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

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Input Needed for Pediatric Data Standards

NCI’s Childhood Cancer Data Initiative seeks your feedback for the Pediatric Cancer Core Common Data Elements, a newly created resource which facilitates data integration and analysis.

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Comment on NIH’s Draft Public Access Policy by August 19

NIH is soliciting comments on a new draft of the Public Access Policy which will remove the 12-month embargo period for NIH-funded manuscripts and data.

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Submit Your Project Pitch to the 2024 Human Tumor Atlas Network (HTAN) Data Jamboree

Bring your ideas for reusing spatial omics and single-cell sequence data! Collaborate with researchers and coders on data interoperability, educational tools, and creative uses of the HTAN atlas data.

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Predicting Cancer Treatment Responses with AI Tool PERCEPTION

Discover a new AI-driven tool that uses single-cell RNA data to help predict patient responses to cancer treatments.

3-D illustration of four cells, shown in blue with large red cell nuclei. Two cells are secreting small round bubbles from their surfaces. These bubbles represent exosomes, small cargo-carrying cell structures that have a part in diseases like cancer.

Seeking a Better Biopsy? NCI-Funded Researchers Are Using Machine Learning to Identify Exosome Biomarkers

NCI-funded researchers used machine learning to characterize a cancer biomarker based on exosomes. Their biomarker worked well using non-invasive sources, such as blood and urine, allowing the researchers to catch cancer early, even in tumors of undetermined origins.

A magnifying glass gives a close-up view of a strand of DNA. The DNA strand is set against a backdrop of buildings and houses, simulating a neighborhood. The illustration shows the value of understanding gene expression across the cell’s neighborhood.

Tool Unlocks Data’s Potential to Learn More About Cell Neighborhoods

See how a new NCI-funded tool is helping scientists learn more about spatial gene expression, enabling them to use these data to better understand how cells relate—with their neighbors and within the tumor environment.

Collage of people from different racial backgrounds. Positioned between each person's face is a depiction of strands of DNA floating through space.

NCI Helps Address Equity, Diversity, and Inclusion at the GA4GH

NCI staff contributed to an October 2023 publication that highlights how the Global Alliance for Genomics and Health (GA4GH) coalition is creating a more equitable, diverse, and inclusive environment within its standards and members. This is to prevent continuing biases in genomics data collection methods and the genomics workforce. By taking actions to address such changes, the GA4GH coalition hopes to inspire others to do the same and make genomic data more representative of the global community.

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Announcing NCI’s New Deputy Director for Data Science and Strategy

NCI has a new data science leader as of June 30, 2024! Meet Dr. Warren Kibbe. He’s bringing a wealth of experience to a brand new position at NCI—with the goal of advancing the use of data and data science in cancer research and care.

Graphic depicts the evaluation of AI. A scale is shown at the center of the graphic. The scale weighs the accuracy of AI (“accuracy” positioned to the left of the scale and depicted as the letters “A-I” within cogwheels) and the results/what’s learned of AI (“results” positioned to the right of the scale and portrayed as a human brain). Arrows encircle the top and bottom of the scale, illustrating a cycle.

Evaluating Diversity in an Artificial Intelligence (AI) Model for Breast Cancer

Can you develop an algorithm that applies to every patient, in every situation? A recent study shows the importance of adding diversity to your data when developing AI models.

The letters "AI" appear in a box. Brightly colored lines feed into the box from all sides, showing how data flows into the box, where the information is then processed by AI.

Interpretable AI Helps Predict the Effectiveness of Immunotherapy

Learn about NCI’s LORIS, a new artificial intelligence-approach to help you predict how a patient will respond to immunotherapy.

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