Developing an in silico minimum inhibitory concentration panel test for Klebsiella pneumoniae
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Summary
This study shows that machine learning can be used to build a complete in silico MIC prediction panel for K. pneumoniae and provides a framework for building MIC prediction models for other pathogenic bacteria.
- Type
- article
- Published
- 2017-09-25
- Cited by
- 171
- References
- 78
- Access
- Open access
- OpenAlex
- https://openalex.org/W2758908936
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:3293646
Keywords
Klebsiella pneumoniae, In silico, Antibiotic resistance, Turnaround time, Minimum inhibitory concentration
References
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- Molecular dissection of the evolution of carbapenem-resistant multilocus sequence type 258 Klebsiella pneumoniae
- Molecular Characterization of Multidrug Resistant Hospital Isolates Using the Antimicrobial Resistance Determinant Microarray
- Alterations in mGluR5 Expression and Signaling in Lewy Body Disease and in Transgenic Models of Alpha-Synucleinopathy – Implications for Excitotoxicity
- Positive Outcomes Influence the Rate and Time to Publication, but Not the Impact Factor of Publications of Clinical Trial Results
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- Antimicrobial resistance and machine learning: past, present, and future
- High-yield and rapid synthesis of ultrathin silver nanowires for low-haze transparent conductors
- Breaking the code of antibiotic resistance
- A k-mer-based method for the identification of phenotype-associated genomic biomarkers and predicting phenotypes of sequenced bacteria
- Precise prediction of antibiotic resistance in Escherichia coli from full genome sequences
- Using machine learning to predict antimicrobial minimum inhibitory concentrations and associated genomic features for nontyphoidal Salmonella
- Interpretable genotype-to-phenotype classifiers with performance guarantees
- Personalizing the Management of Pneumonia.
- Genome-Based Prediction of Bacterial Antibiotic Resistance
- Predicting Antimicrobial Resistance and Associated Genomic Features from Whole-Genome Sequencing
- Prediction of antibiotic resistance in Escherichia coli from large-scale pan-genome data
- A crash course in sequencing for a microbiologist
- VAMPr: VAriant Mapping and Prediction of antibiotic resistance via explainable features and machine learning
- Integrated Analysis of Population Genomics, Transcriptomics and Virulence Provides Novel Insights into Streptococcus pyogenes Pathogenesis
- Inferring phenotypes from genotypes with machine learning : an application to the global problem of antibiotic resistance
- Computational Health Engineering Applied to Model Infectious Diseases and Antimicrobial Resistance Spread
- Using Machine Learning To Predict Antimicrobial MICs and Associated Genomic Features for Nontyphoidal Salmonella
- Quantifying the surveillance required to sustain genetic marker-based antibiotic resistance diagnostics
- Next-Generation Sequencing in Clinical Microbiology: Are We There Yet?
- Using Genomics to Track Global Antimicrobial Resistance
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- МУЛЬТИПЛИКАТИВНАЯ ФУНКЦИЯ ПРИНАДЛЕЖНОСТИ КАК МЕТРИКА ОЦЕНКИ IN SILICO КАРДИОТОКСИЧНОСТИ ХИМИЧЕСКИХ СОЕДИНЕНИЙ
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