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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.

MRI image of an enlarged prostate.

NCI Uses AI to Take the Guesswork Out of Assessing Prostate Cancer Images

NCI researchers are using artificial intelligence (AI) to uncover additional information from medical images. This helps them not only to diagnose cancer but also to predict how it will progress and if it will re-occur.

A strand of DNA is shown inside a brightly colored circle. A second circle highlights a portion of the DNA and is designated by an “+”. Depicts how high-tech tools are being used to better understand genetics.

Machine Learning Approach Helps Interpret Genome-Wide Dynamics

A new, scalable, machine learning model is helping scientists model thousands of transcription factors and genes in the human genome, providing new information on these genes and how they work/change over time.

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.

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.

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.

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.

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.

A DNA strand is seen radiating from a toolbox full of tools. Illustration shows how scientists have many tools for use in DNA analysis.

MuSiCal—A New NCI-Funded Tool for Analyzing Genetic Mutations

Want to identify existing gene mutations more accurately and discover new signatures more efficiently? There’s a new NCI-funded tool, the Mutational Signature Calculator, that can help improve your standard workflow.

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