Microbiology Meets Machine Learning

Artificially intelligent software has human-like ability to analyze host-pathogen interactions in microscopy images.

Written byRuth Williams
| 2 min read
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Computational systems called neural networks—based on the learning processes of biological brains—enable a form of machine learning that has the potential to help researchers interpret biological and medical images. Scientists who study how pathogens interact with host cells are now beginning to harness such technology.

“Most people in the [pathogen-host interactions] field were just manually counting—literally sitting there and assessing how many [parasites] per cell, how many in one of these vacuoles,” and so on, says parasitologist Eva Frickel of the Francis Crick Institute in London. “My students were losing hours and hours, days and weeks counting these events.”

Neural networks are used for all manner of image-processing tasks, such as face recognition, diagnostics, and self-driving cars, so Frickel thought such a system might offer a solution to her team’s problem. She teamed up with computational biologist Artur Yakimovich of the Medical Research Council’s Laboratory ...

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  • ruth williams

    Ruth is a freelance journalist. Before freelancing, Ruth was a news editor for the Journal of Cell Biology in New York and an assistant editor for Nature Reviews Neuroscience in London. Prior to that, she was a bona fide pipette-wielding, test tube–shaking, lab coat–shirking research scientist. She has a PhD in genetics from King’s College London, and was a postdoc in stem cell biology at Imperial College London. Today she lives and writes in Connecticut.

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