Cancer Data Science Pulse

Having trouble staying up to date on cancer variants? CIViC (or Clinical Interpretation of Variants in Cancer) offers an open-source knowledgebase and web interface to help connect researchers to the latest published findings on a full range of variant interpretations.

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.

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.

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.

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.

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.

On November 3, Dr. Duran will present the next Data Science Seminar, “Social Determinants of Health.” This blog offers insight into Dr. Duran’s work and why this topic is important to her.

On Wednesday, September 22, 2021, Yanjun Qi, Ph.D., from the University of Virginia, will present “AttentiveChrome: Deep Learning for Predicting Gene Expression from Histone Modifications,” in the kickoff of the Fall Data Science Seminar Series. This blog offers insight on Dr. Qi’s research and why this topic is important to her.