Glenn Mersky is working at the intersection of biology and machine learning to address key questions in cancer research.
The ‘21 graduate of the Computational Biomedicine and Biotechnology (CoBB) MS program was drawn to CoBB because of its interdisciplinary focus.
“It was the intersection of computers, math and biology that appealed to me,” Mersky said. “I liked having that big translational area where you could kind of bridge the gap between subjects.”
Mersky, who earned a bachelor’s degree in mathematical biology, knew he had chosen the right master’s degree program when he enrolled in the machine learning course with Associate Professor David Koes.
“Every time I would get them (machine learning models) to work, or I would actually see a feasible thing working was really cool,” Mersky said. “You’re not just doing something for a while and thinking ‘I don’t know if this is really going anywhere.’ You had tangible results that you had created and could see come to life.”
During his time in the CoBB program, Mersky also worked in the Drug Discovery Institute, an initiative to discover and develop drugs to treat diseases. His research focused on cell behavior and drug effects of Alzheimer’s disease.
Mersky has applied the skills he learned in the CoBB program to his career as a clinical informatics specialist at Thomas Jefferson University. In this role, he analyzes data to help melanoma researchers work more efficiently.
“I talk with them to get an idea of how to translate that information into code and get actual results,” he said. “So, I do analysis, write out code and work on pipelines to make repetitive tasks in the lab more efficient.”
A key part of Mersky’s job is making data transparent and accessible. He uses techniques and pipelines to classify data.
“It could be something as simple as counting the number of times a transcript occurs or trying to classify or cluster samples based off of their phenotype,” he said. “There’s almost an infinite number of things to do depending on whether you have the right question to ask.”
Mersky says the CoBB program equipped him with tools to answer questions researchers may have about data. His advice to new students entering the program is to find their niche in computational biology.
“There are definitely certain tasks that you’ll be doing day to day that come easier and that you’re excited to do,” he said. “That’s how I felt about machine learning. I had this sense of wonderment like how crazy is it that we can code and do these things that answer such complex questions.”