Cancer Data Science Pulse

Data Sharing

Data and Artificial Intelligence (AI) are a match seemingly made in heaven. By joining data and AI, scientists are able to shift a lot of the burden associated with using data from human to machine. See why the data-AI relationship works so well for cancer research in this offbeat blog featuring two fictitious characters—Datum and his pal Aida.

At the start of the COVID-19 outbreak, NCI’s Frederick National Laboratory for Cancer Research, along with the Centers for Disease Control and Prevention and the National Institute of Allergy and Infectious Diseases, modified an existing tool used for managing NCI’s clinical trials to create a COVID-19 Seroprevalence Hub (SeroHub) to track COVID-19 seroprevalence across the United States. This blog looks at how SeroHub has evolved since the pandemic first began and shows how it could serve as a blueprint for monitoring future infectious disease outbreaks that threaten public health.

CBIIT’s NIH Data and Technology Advancement (DATA) Scholar, Dr. Jay G. Ronquillo, offers a bird’s-eye view of cloud computing, including tips for managing costs, access, and training to help advance precision medicine and cancer research.

In this blog, Dr. Elana J. Fertig describes how she is using artificial intelligence, blended with spatial and single cell technologies, to better understand how cancer will respond to treatment. Predicting the changes that occur in the tumor during treatment may someday enable us to select therapies in advance, essentially stopping the disease in its tracks before it reaches the next stage in its evolution.

Converting the many petabytes of cancer data available on the cloud from information to answers is a complex task. In this blog, Deena Bleich shares how the ISB Cancer Gateway in the Cloud (ISB-CGC), an NCI Cloud Resource, hosts large quantities of cancer data in easily accessible Google BigQuery tables, expediting the process.

This blog offers a primer on semantics, a topic that has broad implications for the biomedical informatics and data science fields. Here, Gilberto Fragoso, Ph.D., describes the structures that serve as a foundation for data science semantics. Those systems help improve data interoperability, allowing researchers to query, retrieve, and combine very different data sets for more extensive analysis.

In this latest Data Science Seminar, Jim Lacey, Ph.D., M.P.H., shares the lessons he learned in transitioning a large cancer epidemiology cohort study to the cloud, including the importance of focusing on people and processes as well as technology. Project managers, principal investigators, co-investigators, data managers, data analysts—really anyone who is part of a team that wants to use the cloud or cloud-based resources for their studies—should attend.

The diversity, complexity, and distribution of data sets present an ongoing challenge to cancer researchers looking to perform advanced analyses. Here we describe the Cancer Genomics Cloud, powered by Seven Bridges, an NCI Cloud Resource that’s helping to bring together data and computational power to further advance cancer research and discovery.

To commemorate the National Cancer Act’s 50th anniversary, we’ve pulled together Five Data Science Technologies poised to make a difference in how cancer is diagnosed, treated, and prevented.

Technological advancements, such as machine learning and artificial intelligence, have made open data sharing more complex and put new pressure on existing laws that protect data privacy. This blog examines the privacy processes and policies that are helping address privacy concerns in today’s ever-changing “big data” landscape.