Understanding and predicting ciprofloxacin minimum inhibitory concentration in <i>Escherichia coli</i> with machine learning

Pataki, Bálint Ármin; Matamoros, Sébastien; Putten, Boas C L van der; Remondini, Daniel; Giampieri, Enrico; Aytan-Aktug, Derya; Hendriksen, René S.; Lund, Ole; Csabai, István; Schultsz, Constance

doi:10.1101/806760

Cited by 10 publications

(9 citation statements)

References 36 publications

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“…Additional issues that need to be considered when developing a reliable and useful prediction model include the fact that genotypes are often geographically clustered. This means that if a prediction model is trained on data from one country, the model might not be generalizable to data from another country [16] . Data from multiple countries potentially must be used in any proposed model.…”

Section: Introductionmentioning

confidence: 99%

AMR-Diag: Neural network based genotype-to-phenotype prediction of resistance towards β-lactams in Escherichia coli and Klebsiella pneumoniae

Avershina

Sharma

Taxt

et al. 2021

Computational and Structural Biotechnology Journal

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Section: Introductionmentioning

confidence: 99%

AMR-Diag: Neural network based genotype-to-phenotype prediction of resistance towards β-lactams in Escherichia coli and Klebsiella pneumoniae

Avershina

Sharma

Taxt

et al. 2021

Computational and Structural Biotechnology Journal

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“…With enough independent observations, hypothesis-free machine learning methods can generate models which predict the phenotype of new isolates and potentially tell us something about the underlying genetic mechanisms. A deluge of recent papers have applied general predictive models to such data sets and have mostly showed high accuracy (3)(4)(5)(6)(7)(8). However, some commentaries have been more cautious in their conclusions (9,10).…”

mentioning

confidence: 99%

Improved Prediction of Bacterial Genotype-Phenotype Associations Using Interpretable Pangenome-Spanning Regressions

Lees

Mai

Galardini

et al. 2020

mBio

130

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ABSTRACT Discovery of genetic variants underlying bacterial phenotypes and the prediction of phenotypes such as antibiotic resistance are fundamental tasks in bacterial genomics. Genome-wide association study (GWAS) methods have been applied to study these relations, but the plastic nature of bacterial genomes and the clonal structure of bacterial populations creates challenges. We introduce an alignment-free method which finds sets of loci associated with bacterial phenotypes, quantifies the total effect of genetics on the phenotype, and allows accurate phenotype prediction, all within a single computationally scalable joint modeling framework. Genetic variants covering the entire pangenome are compactly represented by extended DNA sequence words known as unitigs, and model fitting is achieved using elastic net penalization, an extension of standard multiple regression. Using an extensive set of state-of-the-art bacterial population genomic data sets, we demonstrate that our approach performs accurate phenotype prediction, comparable to popular machine learning methods, while retaining both interpretability and computational efficiency. Compared to those of previous approaches, which test each genotype-phenotype association separately for each variant and apply a significance threshold, the variants selected by our joint modeling approach overlap substantially. IMPORTANCE Being able to identify the genetic variants responsible for specific bacterial phenotypes has been the goal of bacterial genetics since its inception and is fundamental to our current level of understanding of bacteria. This identification has been based primarily on painstaking experimentation, but the availability of large data sets of whole genomes with associated phenotype metadata promises to revolutionize this approach, not least for important clinical phenotypes that are not amenable to laboratory analysis. These models of phenotype-genotype association can in the future be used for rapid prediction of clinically important phenotypes such as antibiotic resistance and virulence by rapid-turnaround or point-of-care tests. However, despite much effort being put into adapting genome-wide association study (GWAS) approaches to cope with bacterium-specific problems, such as strong population structure and horizontal gene exchange, current approaches are not yet optimal. We describe a method that advances methodology for both association and generation of portable prediction models.

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“…The MICs predicted by the models correlated with the presence of known resistance genes, and the overall accuracy of the system was 92e95% within a two-fold dilution. However, in a similar article predicting ciprofloxacin MIC in E. coli, the accuracy was only 65% within a two-fold dilution [65]. Mycobacterium tuberculosis antibiotic susceptibility testing was the aim of seven ML systems, using WGS [66,67] or protein structure data extrapolated from genomic data [68,69].…”

Section: Evaluation Of Antimicrobial Susceptibilitymentioning

confidence: 99%

Machine learning in the clinical microbiology laboratory: has the time come for routine practice?

Peiffer-Smadja

Dellière

Rodriguez

et al. 2020

Clinical Microbiology and Infection

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Background: Machine learning (ML) allows the analysis of complex and large data sets and has the potential to improve health care. The clinical microbiology laboratory, at the interface of clinical practice and diagnostics, is of special interest for the development of ML systems. Aims: This narrative review aims to explore the current use of ML In clinical microbiology. Sources: References for this review were identified through searches of MEDLINE/PubMed, EMBASE, Google Scholar, biorXiv, arXiV, ACM Digital Library and IEEE Xplore Digital Library up to November 2019. Content: We found 97 ML systems aiming to assist clinical microbiologists. Overall, 82 ML systems (85%) targeted bacterial infections, 11 (11%) parasitic infections, nine (9%) viral infections and three (3%) fungal infections. Forty ML systems (41%) focused on microorganism detection, identification and quantification, 36 (37%) evaluated antimicrobial susceptibility, and 21 (22%) targeted the diagnosis, disease classification and prediction of clinical outcomes. The ML systems used very diverse data sources: 21 (22%) used genomic data of microorganisms, 19 (20%) microbiota data obtained by metagenomic sequencing, 19 (20%) analysed microscopic images, 17 (18%) spectroscopy data, eight (8%) targeted gene sequencing, six (6%) volatile organic compounds, four (4%) photographs of bacterial colonies, four (4%) transcriptome data, three (3%) protein structure, and three (3%) clinical data. Most systems used data from high-income countries (n ¼ 71, 73%) but a significant number used data from low-and middle-income countries (n ¼ 36, 37%). Performance measures were reported for the 97 ML systems, but no article described their use in clinical practice or reported impact on processes or clinical outcomes. Implications: In clinical microbiology, ML has been used with various data sources and diverse practical applications. The evaluation and implementation processes represent the main gap in existing ML systems, requiring a focus on their interpretability and potential integration into real-world settings.

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

Cited by 10 publications

References 36 publications

AMR-Diag: Neural network based genotype-to-phenotype prediction of resistance towards β-lactams in Escherichia coli and Klebsiella pneumoniae

AMR-Diag: Neural network based genotype-to-phenotype prediction of resistance towards β-lactams in Escherichia coli and Klebsiella pneumoniae

Improved Prediction of Bacterial Genotype-Phenotype Associations Using Interpretable Pangenome-Spanning Regressions

Machine learning in the clinical microbiology laboratory: has the time come for routine practice?

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