Review examines machine learning concepts for microbiologists

In a review in Nature Reviews Microbiology, ISPH Investigator Levi Waldron and colleagues highlight the increasing importance of machine learning in microbiology, where it is used for tasks such as predicting antibiotic resistance and associating human microbiome features with complex host diseases. 

Together with co-authors from the University of Trento and the European Institute for Oncology in Italy, Waldron examines the main machine learning concepts, tasks, and applications that are relevant for experimental and clinical microbiologists. The review provides the minimal toolbox for a microbiologist to be able to critically evaluate and apply machine learning in their field.

“It was exciting to try to distill the essential concepts of machine learning for a broad audience of microbiologists, and to do it as part of a team with so much expertise,” says Waldron.“I think this review will also be interesting for other public health professionals outside the field of microbiology, who just would like a conceptual, comprehensible, but rigorous overview of machine learning.”

Asnicar, F., Thomas, A.M., Passerini, A. et al. Machine learning for microbiologists. Nat Rev Microbiol (2023).

About the CUNY SPH
The CUNY Graduate School of Public Health and Health Policy (CUNY SPH) is committed to promoting and sustaining healthier populations in New York City and around the world through excellence in education, research, and service in public health and by advocating for sound policy and practice to advance social justice and improve health outcomes for all.

About the CUNY ISPH
The CUNY Institute for Implementation Science in Population Health (ISPH) was founded on the notion that substantial improvements in population health can be efficiently achieved through better implementation of existing strategies, policies, and interventions across multiple sectors. With that in mind, we study how to translate and scale up evidence-based interventions and policies within clinical and community settings in order to improve population health and reduce health disparities.