Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics
Ariane Khaledi, Aaron Weinmann§, Monika Schniederjans, Ehsaneddin Asgari, Tzu‐Hao Kuo, Antonioa Oliver, Gabriel Cabot, Axel Kola, Petra Gastmeier, Michael Hogardt, Daniel E Jonas, Mohammad R. K. Mofrad, Andreas Bremges, Alice Carolyn McHardy, Susanne Häußler · eScholarship (California Digital Library) · 2019
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 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)