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Understanding and predicting ciprofloxacin minimum inhibitory concentration in Escherichia coli with machine learning

Abstract:

It is important that antibiotics prescriptions are based on antimicrobial susceptibility data to ensure effective treatment outcomes. The increasing availability of next-generation sequencing, bacterial whole genome sequencing (WGS) can facilitate a more reliable and faster alternative to traditional phenotyping for the detection and surveillance of AMR. This work proposes a machine learning approach that can predict the minimum inhibitory concentration (MIC) for a given antibiotic, here cipr...

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Publication status:
Published
Peer review status:
Peer reviewed

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Name:
Medical Research Council
Grant:
RN0308
Publisher:
Springer Nature
Journal:
Scientific reports More from this journal
Volume:
10
Issue:
1
Article number:
15026
Publication date:
2020-09-14
Acceptance date:
2020-08-18
DOI:
EISSN:
2045-2322
ISSN:
2045-2322
Pmid:
32929164

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