Cancer Data Science Pulse

Seminar Series

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.

CBIIT’s May 19 Data Science Seminar Series speaker, Dr. Kristen Naegle, took the speed of computational biology, blended it with basic science know-how, and developed an algorithm that is proving to be remarkably effective in predicting kinase activity. Understanding kinases in oncology may help identify people who are more likely to respond (or not respond) to certain medications, further advancing precision medicine.

Dr. Charles Wang offers a sneak peek at his upcoming Data Science Seminar presentation, scheduled for April 7. His recent study provides guidance for choosing an appropriate scRNA-seq platform and software tool for a scRNA-seq study. Using these guidelines, scientists can select the workflow that will yield the most meaningful results.

I have been involved in the design and implementation of cancer research information systems throughout my entire 30-year career. My father was the principal designer of the Apollo Lunar Descent Guidance and Navigation software that landed the first men on the moon in the late 1960's. Growing up in the Boston area, I became intensely interested in his work and spent many weekends tagging along with him in the MIT mainframe computer laboratory.

Biomedical knowledge is typically centered around the variety of biological entity types, such as genes, genetic variants, drugs, diseases, etc. Collectively, we refer to them as "BioThings." The volume of biomedical data has grown explosively, thanks to the efforts of many different researchers and consortia. This explosive growth includes many different types of data using many different formats and standards, making it difficult to unify the disparate sources of 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.