Students tackle drug resistance by teaching machine learning
SFSU researchers have published a step-by-step tutorial for applying machine learning to drug resistance
Antimicrobial resistance is a growing health crisis that could lead to millions of deaths by 2050, according to the World Health Organization. Antibiotics are critical for human health, but many microbes are evolving resistance to one or more drugs. San Francisco State University researchers are among those using machine learning to predict drug resistance in patients. And they’re trying to remedy a related problem, too: the lack of resources that teach how to use machine learning to detect antibiotic resistance.
In a new paper in PLOS Computational Biology, the SFSU team published a step-by-step machine learning tutorial for beginners. Other than Biology Professor Pleuni Pennings, the remaining seven researchers on the paper were undergraduate, graduate students and post-baccalaureate students; many were first-time researchers, and nearly all were new to machine learning.
“We wanted to do a tutorial paper instead [of a research paper] because we thought it was more important to put out a teachable resource. We struggled to find one, so we wanted to make our own,” said co-first author Faye Orcales (B.S., ’21), who worked on the project as a post-bac.
As beginners from a variety of backgrounds, the team made sure the paper would be accessible to their student peers and educators in biology and chemistry as well as anyone in health sciences. Though the lesson is beginner friendly, the authors recommend having introductory coding knowledge, something that is beyond the scope of this paper.
“Because it’s in a peer-reviewed journal, it makes it feel real because other scientists — not just your professor or friends — reviewed the article. The peer review process was crucial because it gives other perspectives,” said co-first author Lucy Moctezuma, a Statistics graduate student at CSU East Bay who has a background in psychology. She joined Pennings’ SFSU lab through a friend and was part of the lab for nearly three years. She and Orcales led the effort to write the manuscript and address any feedback. “We were a bunch of students trying to figure it out and we were able to! I think that we should all be proud of that,” Moctezuma said.
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