Table 3 Characteristics of six studies on the pathogens of periprosthetic joint infection
First author [Ref.] | Articles | Journal | Year | Number and pathogens isolated |
|---|---|---|---|---|
Davis, J.J. [14] | Antimicrobial resistance prediction in pathosystems resource integration center and rapid annotation using subsystem technology | Scientific Report | 2016 | 606 Staphylococcus aureus |
Drouin, A. [15] | Interpretable genotype-to-phenotype classifiers with performance guarantees | Scientific Report | 2019 | 1593 Staphylococcus aureus, 134 Enterococcus faecium, 1524 Escherichia coli, 2107 Klebsiella pneumoniae, 491 Pseudomonas aeruginosa |
Moradigaravand, D. [16] | Prediction of antibiotic resistance in Escherichia coli from large-scale pan-genome data | PLoS Computational Biology | 2018 | 1936 Escherichia coli |
Nguyen, M. [17] | Developing an in silico minimum inhibitory concentration panel test for Klebsiella pneumoniae | Scientific Report | 2018 | 1668 Klebsiella pneumoniae |
Khaledi, A. [18] | Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics | EMBO Molecular Medicine | 2020 | 414 Pseudomonas aeruginosa |
Aun, E. [19] | A k-mer-based method for the identification of phenotype-associated genomic biomarkers and predicting phenotypes of sequenced bacteria | PLoS Computational Biology | 2018 | 200 Pseudomonas aeruginosa |