Cancer Data Science Pulse

The Cancer Data Science Pulse blog provides insights on trends, policies, initiatives, and innovation in the data science and cancer research communities from professionals dedicated to building a national cancer data ecosystem that enables new discoveries and reduces the burden of cancer.

Illustration of a digitally rendered cell.

Meteorology, Aerospace Engineering, and Cancer Research—The Future of Predictive Modeling

What do winter storms, airplanes, and cancer research have in common? In this blog, experts on meteorology, aerospace engineering, and radiation oncology explore what we can learn from these very different fields to further advance how we target and apply radiation to more effectively treat cancerous tumors.

Abstract image of DNA strand off to the left with a doctor or scientist off to the right, wearing a face mask, tie, lab coat, stethoscope around the neck, and holding a placard reading 2030.

In The Year 2030—Looking at How Genomic Data Might Evolve

Imagine a day when your healthcare is so personalized that there’s no guessing as to what medication will work best for you or whether you are at risk for a particular disease. This is a bold prediction recently addressed by genomic experts, Dr. Karen Miga and Dr. Evan Eichler. This blog examines how advances in technology are drawing us closer to a time when genomic information becomes a routine part of every patient’s healthcare.

Illustration features two histology slides and shows how a human interprets the images compared with a computer.

Machine Learning and Computer Vision Offer a New Way of Looking at Cancer

Artificial Intelligence offers boundless possibilities, especially in the healthcare field. In a recent CBIIT Data Science Seminar, Dr. James Zou showed how Computer Vision (CV) is helping create a new data-driven “language of morphology” that allows researchers to be more precise in interpreting histological images. Just as computers help propel self-driving cars along busy roadways, CV offers a faster, less-subjective method for assessing disease.

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