This month’s Genome Watch examines how novel machine learning-enabled molecular diagnostic approaches can predict antibiotic resistance when genetic variation falls short.
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27 March 2020
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References
Bradley, P. et al. Rapid antibiotic-resistance predictions from genome sequence data for Staphylococcus aureus and Mycobacterium tuberculosis. Nat. Commun. 6, 10063 (2015).
Chen, M. L. et al. Beyond multidrug resistance: leveraging rare variants with machine and statistical learning models in Mycobacterium tuberculosis resistance prediction. EBioMedicine 43, 356–369 (2019).
Martin, L. W. et al. Expression of Pseudomonas aeruginosa antibiotic resistance genes varies greatly during infections in cystic fibrosis patients. Antimicrob. Agents Chemother. 62, e01789–18 (2018).
Khaledi, A. et al. Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics. EMBO Mol. Med. 12, e10264 (2020).
Belliveau, N. M. et al. Systematic approach for dissecting the molecular mechanisms of transcriptional regulation in bacteria. Proc. Natl Acad. Sci. USA 115, E4796–E4805 (2018).
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Wheeler, N.E., Sánchez-Busó, L., Argimón, S. et al. Lean, mean, learning machines. Nat Rev Microbiol 18, 266 (2020). https://doi.org/10.1038/s41579-020-0357-4
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DOI: https://doi.org/10.1038/s41579-020-0357-4
