News

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

A circle contains a syringe, a pill bottle, and a strand of DNA—illustrates how researchers are using genetics to find new and better ways of treating cancer.

Machine Learning Models Help Predict Medication Responses

What if you could predict how a chemotherapy drug would work—in terms of sensitivity and side-effects—before you ever use it? NCI-funded researchers are using machine learning models to better understand a key mechanism underlying cancer, giving us new ways to predict responses to common chemotherapy drugs.

Scientists hand holds an illustrated set of lungs. The lungs are illuminated and include bright pinpoints of light. Illustrates the idea of using AI to better understand lung disease.

Model Uses Blood Proteins to Predict Lung Cancer Risk

A new algorithm is showing promising results in predicting lung cancer. See how proteins in your blood may someday help determine your risk for lung cancer.

two stylized people from the chest up next to each other. One is filled with interconnected data points, the other is a flat peach tone. Behind them is an infinity symbol.

Funding Available to Support Development of Digital Twins in Radiation Oncology

Do you work with digital twin data models? If so, consider applying by March 21, 2024, for research funding!

A tissue cell neighborhood shows four groups of cells, featuring different cell types, including immune cells and tumor cells. Lines connect the cells and cell groups, showing how cells interact, both with their closest neighbors and with cell groups that are farther away.

Algorithm Shines Light on Cancer Cell Neighborhoods

The ways that cells interact with neighboring cells and their environments can either regulate or promote how tumors grow and spread. See how a new tool is helping us better understand these important “neighborhood” interactions.

Artists conceptualization of a digital twin. Shows the mirrored image of two heads. The only difference is one head is filled in with dots whereas the second head has spaces missing.

New Report Spotlights Research Gaps and Future Directions for Digital Twins

Interested in digital twin technology? Read this report for a “reality check” on what is currently known about the technology and what’s still missing.

A strand of DNA is featured, with two puzzle pieces fitting into place on each strand. Illustrates how researchers are fitting together all the pieces of what we know about genetics to get a more complete understanding of our DNA.

Epigenetics and Machine Learning Help Make Cancer Predictions

NCI-funded researchers are using a machine learning approach to predict cancer outcomes based on epigenetic data, which take into account both environmental and genetic influences.

Illustration of a man’s upper torso, highlighting the lungs. In the background are dots and dashes to signify data. Concept is used to show the value of data in assessing lung disease.

Deep Learning Model Helps Predict Lung Cancer

A new deep learning model, called CXR-Lung-Risk, proved useful in identifying people at risk for dying from lung cancer or other lung disease, based on a single X-ray image.

Data points and lines are used to illustrate the figure of a woman, with highlighted circles to indicate breasts and the acronym “AI” appearing at the top.

Multidisciplinary Team Develops Model to Predict Breast Cancer Outcomes

Are you interested in developing a machine learning model to predict disease? Get advice from researchers who built a successful model for predicting breast cancer outcomes.

young child with bangs and space buns wearing a yellow shirt

Eight Research Projects Receive Funding from NCI’s Childhood Cancer Data Initiative

The Childhood Cancer Data Initiative (CCDI) awarded administrative supplements to eight NCI-Designated Cancer Centers, enabling research that leverages the CCDI Data Ecosystem to address scientific questions and create analytical tools for advancing childhood cancer research.

Circular illustration shows the concept of a Genome Wide Association Study. The circle’s outlined by small blue, orange, green, and black stick figures representing a population. Within that circle is an orange and green string of DNA. At the center of the circle are the basic amino acids (ATGCA) and five black stick figures, illustrating the individual.

New Approach Helps Address Lack of Diversity in GWAS Cancer Data

In a recent study, NCI’s Dr. Haoyu Zhang describes CT-SLEB—a powerful and computationally scalable method for generating more precise polygenic risk scores across a range of ancestral groups (including Latino, African American, East Asian, and South Asian).

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