Machine learning for microbial identification and antimicrobial susceptibility testing on MALDI-TOF mass spectra: a systematic review
Caroline Weis(GlaxoSmithKline (India)), Karsten Borgwardt(Max Planck Institute of Biochemistry), Catherine R. Jutzeler(SIB Swiss Institute of Bioinformatics)
Cited by 214
Related Papers
Direct antimicrobial resistance prediction from clinical MALDI-TOF mass spectra using machine learning
|Nature Medicine|2022|269
Comorbidities, clinical signs and symptoms, laboratory findings, imaging features, treatment strategies, and outcomes in adult and pediatric patients with COVID-19: A systematic review and meta-analysis
|Travel Medicine and Infectious Disease|2020|155
Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World
|Chemical Research in Toxicology|2025|99
Phylogenetic Tools for Generalized HIV-1 Epidemics: Findings from the PANGEA-HIV Methods Comparison
|Molecular Biology and Evolution|2016|71
Large-scale DNA-based phenotypic recording and deep learning enable highly accurate sequence-function mapping
|Nature Communications|2020|69