ArticleComputational and structural biotechnology journal2021
Bacterial species identification using MALDI-TOF mass spectrometry and machine learning techniques: A large-scale benchmarking study.
Article in Computational and structural biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of machine learning on clinical MALDI-TOF MS.Briefings in bioinformatics · 2026Pooled it
- Dereplication-assisted culturomics enables strain-level ecological analysis of the human gut microbiome.Gut microbes · 2026Article
- Enhanced classification and identification of bacterial and viral microorganisms by integration of MALDI-TOF mass spectrometry with artificial intelligence.Scientific reports · 2026Article
- Benchmarking deep learning architectures for MALDI-TOF mass spectrometry in infectious disease diagnostics and vector-borne disease surveillance.NPJ digital medicine · 2026Article
- MALDI-TOF MS Classification of Formosan Thraustochytrids and Their Use in Shrimp Farming.Journal of agricultural and food chemistry · 2026Article
- Antibiotic Resistance Detection and Concomitant Species Identification of ESKAPE Pathogens by Proteomics.Molecular & cellular proteomics : MCP · 2026Article
- Diagnosis of leptospirosis in animals: challenges and perspectives.Journal of clinical microbiology · 2026Review
- ReShuffle-MS: Region-Guided Data Augmentation Improves Artificial Intelligence-Based Resistance Prediction inMicroorganisms · 2026Article
- Cell-Free but potent: Antimicrobial potential of Lactobacillus supernatants against multidrug-resistant pathogens.PloS one · 2026Article
- A dual-phase strategy to integrate the VITEK MS RUO database into routine clinical practice: from validation of analytical performance to implementation of an automated workflow.Frontiers in cellular and infection microbiology · 2026Article
- Pathogenicity and virulence ofVirulence · 2025Review
- Genome-scale prediction of gene ontology from mass fingerprints reveals new metabolic gene functions.Life science alliance · 2025Article
- Deep learning-based analysis and identification of single-particle mass spectra of bacteria.Analytical and bioanalytical chemistry · 2025Article
- Enabling next-generation anaerobic cultivation through biotechnology to advance functional microbiome research.Nature biotechnology · 2025Review
- First Case Report of Successful Treatment ofAnimals : an open access journal from MDPI · 2025Article
- End-To-End Deep Learning Explains Antimicrobial Resistance in Peak-Picking-Free MALDI-MS Data.Analytical chemistry · 2025Article
- A MALDI-ToF mass spectrometry database for identification and classification of highly pathogenic bacteria.Scientific data · 2025Article
- Multi-label classification to predict antibiotic resistance from raw clinical MALDI-TOF mass spectrometry data.Scientific reports · 2024Article
- NeuralBeds: Neural embeddings for efficient DNA data compression and optimized similarity search.Computational and structural biotechnology journal · 2024Article
- An antimicrobial drug recommender system using MALDI-TOF MS and dual-branch neural networks.eLife · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Today machine learning methods are commonly deployed for bacterial species identification using MALDI-TOF mass spectrometry data. However, most of the studies reported in literature only consider very traditional machine learning methods on small datasets that contain a limited number of species. In this paper we present benchmarking results on an unprecedented scale for a wide range of machine learning methods, using datasets that contain almost 100,000 spectra and more than 1000 different species. The size and the diversity of the data allow to compare three important identification scenarios that are often not distinguished in literature, i.e., identification for novel biological replicates, novel strains and novel species that are not present in the training data. The results demonstrate that in all three scenarios acceptable identification rates are obtained, but the numbers are typically lower than those reported in studies with a more limited analysis. Using hierarchical classification methods, we also demonstrate that taxonomic information is in general not well preserved in MALDI-TOF mass spectrometry data. For the novel species scenario, we apply for the first time neural networks with Monte Carlo dropout, which have shown to be successful in other domains, such as computer vision, for the detection of novel species.
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