News

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

Illustrated liver shows points of light, connected by brightly colored lines. Signifies how researchers are using data and technology to better understand liver cancer.

Machine Learning Helps Boost Accuracy for Liver Cancer Screening

With the help of machine learning, NCI-funded researchers were able to boost the prognostic power of a common blood test for liver cancer.

Artist illustration of lungs set against a background of a stained pathology slide.

NCI-Funded Researchers Develop a New Model for Interpreting Pathology

Interpreting whole slide images can be a labor intensive and difficult task. A recent article describes a new approach that helps classify cancer and predict how it will progress.

Magnifying glass with the word “Bias” written on it hovers over a group of figures. Depicts the importance of searching for (and correcting) bias when developing a machine learning model.

Algorithm Helps Address Bias in Machine Learning

Are you developing machine learning and looking for ways to make your model generalizable to a diverse population? A recent study describes an algorithm that centers on transforming your data rather than tweaking your model.

Scan of the human body with AI logo

NCI Study Shows AI-Assisted Contouring Improves Prostate Cancer Treatment Efficacy

In the study, artificial intelligence (AI)-assisted contours outperformed manual contours, indicating the technology’s potential to enhance treatment strategies and improve outcomes for patients.

Image of a young girl smiling ahead with the text "Feedback Needed" appearing across her body.

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.

Image of a person typing on a laptop with article icons superimposed in the foreground.

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.

Men and women walk among large screens displaying health related information.

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.

two people, one in a lab coat one in regular clothes, looking at a tablet. Data themed icons in background.

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.

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