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.

The body’s microbiome has a profound effect on health and disease, including cancer. Read how the field of bioinformatics is helping define the microbiome’s composition and function, which could lead to new ways of treating cancer.

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.

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.

Dr. Tony Kerlavage, director of NCI’s Center for Biomedical Informatics and Information Technology (CBIIT), sat down to discuss one key component of racial inequality, the issue of health disparities, as it relates to Big Data. As noted by Dr. Kerlavage, representing our diverse U.S. population in research and in the workforce are key, but we also need better data.

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 is the second of a series of posts that discuss the principles underlying the three-year collaborative program “Joint Design of Advanced Computing Solutions for Cancer (JDACS4C).”

This is the first of a series of posts that discuss the pilot collaborative program “Joint Design of Advanced Computing Solutions for Cancer (JDACS4C)” being pursued by the National Cancer Institute (NCI) and the Department of Energy (DOE).

Biomedical research is evolving with an increasing emphasis on data science, e.g.,