Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics
Year: 2019
Type: dataset
Abstract: Datasets for manuscript "Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics" Metadata.zip 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 We provide the processed molecular data as Numpy compressed files (npz.). You can use the Numpy load method to read in these tables https://docs.scipy.org/doc/numpy/reference/generated/numpy.load.htm. The row (strains_list) and column labels (feature_lists) are stored separately. 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) 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) 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)
Source: Figshare
Institutions: Helmholtz Centre for Infection Research, Center for Experimental and Clinical Infection Research, German Center for Infection Research, University of California, Berkeley, Health Research Institute of the Balearic Islands, Hospital Universitario Son Espases, Charité – Universitätsmedizin Berlin, University Hospital Frankfurt, University of Freiburg, University Medical Center Freiburg, Lawrence Berkeley National Laboratory
Language: English
Subfield: Molecular Medicine
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W4393834290