Lean, mean, learning machines
- PMID: 32203299
- DOI: 10.1038/s41579-020-0357-4
Lean, mean, learning machines
Comment on
-
Beyond multidrug resistance: Leveraging rare variants with machine and statistical learning models in Mycobacterium tuberculosis resistance prediction.EBioMedicine. 2019 May;43:356-369. doi: 10.1016/j.ebiom.2019.04.016. Epub 2019 Apr 29. EBioMedicine. 2019. PMID: 31047860 Free PMC article.
-
Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics.EMBO Mol Med. 2020 Mar 6;12(3):e10264. doi: 10.15252/emmm.201910264. Epub 2020 Feb 12. EMBO Mol Med. 2020. PMID: 32048461 Free PMC article.
References
-
- Bradley, P. et al. Rapid antibiotic-resistance predictions from genome sequence data for Staphylococcus aureus and Mycobacterium tuberculosis. Nat. Commun. 6, 10063 (2015). - DOI
-
- 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). - DOI
-
- 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). - DOI
-
- Khaledi, A. et al. Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics. EMBO Mol. Med. 12, e10264 (2020). - DOI
-
- 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). - DOI
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources
Medical