ArticleAnalytical chemistry2020
Adaption of the Aristotle Classifier for Accurately Identifying Highly Similar Bacteria Analyzed by MALDI-TOF MS.
Article in Analytical chemistry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 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
- Accurate noise-robust classification of Bacillus species from MALDI-TOF MS spectra using a denoising autoencoder.Journal of integrative bioinformatics · 2023Article
- Serum amino acids quantification by plasmonic colloidosome-coupled MALDI-TOF MS for triple-negative breast cancer diagnosis.Materials today. Bio · 2022Article
- MALDI-TOF mass spectrometry of saliva samples as a prognostic tool for COVID-19.Journal of oral microbiology · 2022Article
- Improved Discrimination of Disease States Using Proteomics Data with the Updated Aristotle Classifier.Journal of proteome research · 2021Article
- The local-balanced model for improved machine learning outcomes on mass spectrometry data sets and other instrumental data.Analytical and bioanalytical chemistry · 2021Article
- Identification and dereplication of endophytic Colletotrichum strains by MALDI TOF mass spectrometry and molecular networking.Scientific reports · 2020Article
- How to Apply Supervised Machine Learning Tools to MS Imaging Files: Case Study with Cancer Spheroids Undergoing Treatment with the Monoclonal Antibody Cetuximab.Journal of the American Society for Mass Spectrometry · 2020Article
- Time to Positivity as a Prognostic Tool in the Performance of Short-Term Subculture for MALDI-TOF MS-Based Identification of Microorganisms from Positive Blood Cultures in Pediatric Patients.Current microbiology · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
MALDI-TOF MS has shown great utility for rapidly identifying microbial species. It can be used to successfully type bacteria and fungi from a variety of sources more rapidly and cost-effectively than traditional methods. One area where improvements are necessary is in the typing of highly similar samples, such as those samples from the same genus but different species or samples from within a single species but from different strains. One promising way to address this current limitation is by using advanced machine learning techniques. In this work, we adapt a newly developed machine learning tool, the Aristotle Classifier, to bacterial classification of MALDI-TOF MS data. This tool was originally developed for classifying glycomics and glycoproteomics data, so we modified it to be well-suited for assigning mass spectral data from bacterial proteins. The classifier exceeds existing benchmarks in classifying bacteria, and it shows particularly strong performance when the samples to be identified are highly similar. The combination of mass spectrometry data and tools like the Aristotle Classifier could ameliorate the ambiguities associated with challenging bacterial classification problems.
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Registered trials
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