Cancer Data Science Pulse

Data have been the driving force behind a number of important scientific discoveries. In this latest blog, Dr. Jerry Li describes how data helped power technological advances to unravel the human genome. What’s the next big advance? According to Dr. Li, the blending of data and artificial intelligence is the fastest moving area of research and has the potential to once again revolutionize scientific discovery.

Did you ever wonder what goes into making data ready for analysis by researchers around the world? Introducing “Datum.” This single speck of data was conceptualized to show how NCI’s Center for Biomedical Informatics and Information Technology supports cancer research by bringing data to life.

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.

“Count Me In” (CMI) is a unique project that gives patients an opportunity to share their cancer-related data directly with scientists. According to Corrie Painter, associate director of CMI, this is a largely untapped but vital part of data science. Here she describes the project and what it could mean for future research efforts.

The explosion of genetic information and direct access to large-scale genomic data not only opens up new areas for exploring today's most pressing research questions, it also serves as a reminder of the importance of collaboration at every stage of the study. NCI’s Dr. Daoud Meerzaman describes a new "circular" way of collaborating that keeps everyone in the loop when devising new genomics studies.

Dr. Jaime M. Guidry Auvil serves as the director of the newly-launched NCI Office of Data Sharing (ODS). Headquartered at the Center for Biomedical Informatics and Information Technology, ODS is creating a comprehensive

One of the most exciting developments of the past decade has been the success of methods broadly described as deep learning. While the roots of deep learning date back to early machine learning research of the 1950s, recent improvements in specialized computing hardware and the availability of labeled data have led to significant advances and have shattered performance benchmarks in tasks like image classification and language processing.

This blog post, the fifth, concludes our series that discusses the principles underlying the collaborative project "Joint Design of Advanced Computing Solutions for Cancer (JDACS4C)."

In 2016, a Blue Ribbon Panel (BRP) was established, as part of the Beau Biden Cancer Moonshot, to make key recommendations that would support the Moonshot goals of accelerating progress in cancer research and breaking down barriers to developing new treatments. The Enhanced

In the past year, the use of Artificial Intelligence (AI) in radiology, also called "radiomics," has been getting a lot of attention, mainly because of the progress Deep Learning (DL) has made from a sub-human performance to performance that is similar, or in some cases superior, to that of humans.