Cancer Data Science Pulse

On March 23, Dr. Ben Raphael will present the next Data Science Seminar, “Quantifying tumor heterogeneity using single-cell and spatial sequencing.” In this blog, Dr. Raphael describes how he’s using this technology to dig deeper into the complexity of cancer.

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.

To celebrate the “International Day of Women and Girls in Science,” we asked CBIIT’s Associate Director for Informatics and Data Science, Dr. Jill Barnholtz-Sloan, to share her experience as a woman in data science. She tells about her journey to CBIIT and underscores the value of adding women’s perspectives to the data science field.

Staying afloat in today’s torrent of data sets, tools, and applications calls for both a deep knowledge of cancer, as well as the know-how to apply highly specialized technological solutions. A new resource from NCI’s Informatics Technology for Cancer Research program gives researchers, with varying skills and experience, the training they need to manage technology-driven approaches to cancer research and care.

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.