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Published September 29, 2019 | Version v1
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Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics

Creators

  • 1. Molecular Bacteriology, Helmholtz Centre for Infection Research, Braunschweig, Germany AND Molecular Bacteriology, TWINCORE, Centre for Experimental and Clinical Infection Research, Hannover, Germany
  • 2. Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany AND Molecular Bacteriology, Helmholtz Centre for Infection Research, Braunschweig, Germany AND Molecular Bacteriology, TWINCORE, Centre for Experimental and Clinical Infection Research, Hannover, Germany AND German Center for Infection Research (DZIF), Braunschweig, Germany
  • 3. Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany AND Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, Berkeley, USA
  • 4. Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany
  • 5. Servicio de Microbiología and Unidad de Investigación Hospital Universitario Son Espases, Instituto de Investigación Sanitaria Illes Balears (IdISPa), Palma de Mallorca, Spain
  • 6. Charité - Universitätsmedizin Berlin, Institute of Hygiene and Environmental Medicine, Berlin, Germany
  • 7. Institute of Medical Microbiology and Infection Control, University Hospital Frankfurt, Frankfurt/Main, Germany
  • 8. Institute for Infection Prevention and Hospital Epidemiology, Faculty of Medicine, Medical Center - University of Freiburg, Freiburg, Germany
  • 9. Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Lab, Berkeley, USA AND Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, Berkeley, USA
  • 10. Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany AND German Center for Infection Research (DZIF), Braunschweig, Germany

Description

Datasets for manuscript "Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics"

Metadata.zip

  1. phenotypes.txt: tabular file containing binary resistance phenotypes based on CLSI guidelines, where the rows are the isolates and the columns correspond to different drugs. Resistance : 1, susceptibility: 0, missing: intermediate resistant

Features_gpa_exp_snps.zip  

  1. genexp: gene expression table directory
    • genexp_feature_vect.npz: The feature matrix in the numpy format
    • genexp_feature_list.txt: The columns of the feature matrix (features)
    • genexp_strains_list.txt: The rows of the feature matrix (isolates)
  2. gpa: gene presence/absence table directory
    • gpa_feature_vect.npz: The feature matrix in the numpy format
    • gpa_feature_list.txt: The columns of the feature matrix (features)
    • gpa_strains_list.txt: The rows of the feature matrix (isolates)
  3. snps: SNPs table directory
    • snps_feature_vect.npz: The feature matrix in the numpy format
    • snps_feature_list.txt: The columns of the feature matrix (features)
    • snps_strains_list.txt: The rows of the feature matrix (isolates)

Files

features_gpa_expr_snps.zip

Files (27.7 MB)

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Additional details

Related works

Is part of
Preprint: 10.1101/643676 (DOI)