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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.

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.

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.

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 person wearing a stethoscope sits at a lap top computer. Above the keyboard are a number of small icons and dotted lines, signifying artificial intelligence. The letters "AI" appear just above the keyboard.

Artificial Intelligence Aids in Predicting Prostate Cancer

See how NCI researchers are using artificial intelligence to develop tools that may someday help oncologists make more informed decisions when caring for people with prostate cancer.

An illustrated colon is shown, cradled in the outstretched hands of a doctor wearing a white lab coat.

Machine Learning Helps Refine a New Approach for Early Colorectal Cancer Screening

NCI-funded researchers are using machine learning to help identify an early-warning screening approach for colorectal cancer.

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.

Model Helps Predict Protein’s Immune-Boosting Ability

Are you researching immunotherapies and having trouble predicting which epitope will deliver the biggest punch? Try this new model for predicting immunogenicity, which is reportedly outperforming current models.

Teal DNA strand made up of code

New Notebook Demonstrates Machine Learning in Google BigQuery Using Updated Mitelman Database

In the “Mitelman Gene Fusions in TCGA” notebook, ISB Cancer Gateway in the Cloud (an NCI Cloud Resource) identified the most common gene fusions in prostate adenocarcinoma, demonstrating machine learning in Google BigQuery.

Abstract illustration showing breast cancer and how it might spread to nearby lymph nodes.

Machine Learning Blends Imaging and Clinical Data to Refine Breast Cancer Care

A new machine learning model blends image data with clinical and pathological information to tailor care for patients with breast cancer.

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