Cancer Data Science Pulse

Meet the people who are breaking new ground in the data science field, whether it’s a new tool, a new model, or a completely new way of using data. Here, we’re featuring Svitlana Volkova, Ph.D., chief scientist at Pacific Northwest National Laboratory. She’ll describe how she’s using “foundation models” to give scientists and analysts a new tool for unleashing the power of artificial intelligence (AI).

Whether you are in the data science field, interested in developing computational solutions for clinical oncology, or a clinical researcher, we’ve curated a list of data sets, tools, and learning resources to showcase how these disciplines can and are working together to empower cancer research.

On April 20, Dr. Clemens Grassberger will present the next Data Science Seminar, “Computational and Mathematical Approaches to Modeling Immunotherapy-Radiotherapy Combinations.” Here, Dr. Grassberger describes how combining these two very different therapies—radiation and immunotherapy—may lead to stronger, more effective ways of treating cancer.

On April 6, Dr. Malachi Griffith will present the next Data Science Seminar, “Bioinformatics Approaches for Neoantigen Identification and Prioritization.” Here, Dr. Griffith tells how his tinkering with computers, bioinformatics, and genomics is helping him understand the complexities of this promising research area. If successful, neoantigen-based cancer therapies could prove to be the pinnacle of personalized medicine.

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.

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.

In this blog, University of Maryland's Mrs. Aya Abdelsalam Ismail examines the use of Deep Learning in medical applications, especially as a means for following a disease or disorder over time. She’ll describe how a “wrong turn” in her research on forecasting Alzheimer’s Disease led her to question her model’s performance. Her findings are particularly relevant for Deep Learning models in the cancer field, which use images obtained from patients, often at different points in time.

Get to know David Kepplinger, Ph.D., who will present the next Data Science Seminar, “Robust Prediction of Stenosis from Protein Expression Data. ” In this Q&A, he describes who should attend the talk, how his topic relates to cancer, and why it’s important to delve into unexpected data values when conducting biostatistical analysis.