A new method for identifying strains of bacteria and guessing their resistance to antibiotics uses an AI model to analyze their growth dynamics in culture. Credit: Duke University Biomedical engineers at Duke University have shown that different strains of the same bacterial pathogen can be distinguished by a machine learning analysis of their growth dynamics alone, which can then also accurately predict other traits such as resistance to antibiotics. The demonstration could point to methods for identifying diseases and predicting their behaviors that are faster, simpler, less expensive and more accurate than current standard techniques.
The results appear online on August 3 in the Proceedings of the National Academy of Sciences .
For most of the history of microbiology, bacteria identification has relied on growing cultures and analyzing the physical traits and behaviors of the resulting bacterial colony. It wasn’t until recently that scientists could simply run a genetic test.
Genetic sequencing, however, isn’t universally available and can often take a long time. And even with the ability to sequence entire genomes, it can be difficult to tie specific genetic variations to different behaviors in the real world.
For example, even though researchers know the genetic mutations that help shield/protect bacteria from beta-lactam […]
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