News

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

Tissue biopsy segmentation of a human lung. Blue and green shading shows different cell types and structures. Cell boundaries are outlined in white.

All-in-One Model Helps Identify, Classify, and Analyze Cancer Tumor Tissue

Interpreting tissue biopsies may be easier using this NCI-funded tool. It features an end-to-end approach for automatically analyzing cancer cells and tissues.

Abstract view of the Earth with the letters AI in the center and icons of people spread out across it.

Large Language Models Help Match Patients to Clinical Trials

Learn how NIH and NCI researchers developed TrialGPT, an artificial intelligence algorithm that could help clinicians connect patients to clinical trial opportunities more efficiently.

In the foreground, stacked red blood cells. In the background, numbers, letters, and mathematical equations.

Machine Learning Identifies Clonal Hematopoiesis Driver Mutations

Learn how researchers used machine learning models to identify the specific genetic mutations that trigger clonal hematopoiesis. In this condition, mutated hematopoietic stem cells multiply more rapidly, increasing the risk of blood cancers.

Double stranded DNA forms a question mark. Denotes the study of genetics for better understanding how cancer develops and progresses.

NCI-Funded Researchers Test Platform to Help Reveal Non-Coding Genes Involved in Cancer

NCI-funded researchers are testing a new platform that blends statistical and deep learning models, giving you a fuller picture of the variants driving cancer progression.

A cancer cell is highlighted in orange against a blue background that consists of other cancer and immune cells. The orange cell is in crosshairs, indicating that it’s being targeted for destruction.

DREAM Challenge Benchmarks Approaches for Deciphering Bulk Genetic Cancer Data

Deciphering bulk data is challenging. See how a recent DREAM challenge is helping researchers benchmark bioinformatics and data science approaches for unraveling bulk genetic cancer data.

Spyglass hovers over a strand of double helix strand of DNA.

NCI-Funded Algorithm Helps Track Down Coding and Non-Coding Cancer-Causing Genes

Are you investigating structural variations underlying cancer-causing genes? NCI-funded researchers are testing a new algorithm that could help you track down both coding and non-coding cancer-causing genes.

Silhouette of a dog and little girl sitting in the grass and facing one another.

Machine Learning Reveals Shared Traits of Osteosarcomas in Pet Dogs and Humans

Looking for new data sources for your machine learning model? NCI researchers combined data from dogs and people to identify risk factors for osteosarcoma.

MRI image of an enlarged prostate.

NCI Researchers Test Generalizability of Artificial Intelligence (AI) Model

The hallmark of a good AI model is its ability to work the same in different groups, settings, and situations. See how these NCI researchers used in-house and external images to test their prostate model’s generalizability.

Colored lines and shapes are highlighted against a dark background. Depicts the topic, "bioinformatics,” a technology that uses a computational approach to find new healthcare solutions.

NCI Researchers Reflect on the Cancer Informatics Field

Want to learn more about bioinformatics? Tap into these two newly published articles to see what some prominent researchers are saying about the field and where it’s headed.

Tool is fitted inside a cog, shown against a background of data points connected by lines. Denotes a data science toolkit.

ARPA-H Biomedical Data Fabric (BDF) Toolbox Program Announces Awardees

Construction of the Advanced Research Projects Agency for Health’s (ARPA-H’s) BDF Toolbox program is well underway. ARPA-H has awarded contracts to nearly 20 teams—representing academia, nonprofits, and commercial organizations—who are tackling a broad range of projects, many of which are directly related to cancer research.

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