ArticleMetabolomics : Official journal of the Metabolomic Society2019
A comparative evaluation of the generalised predictive ability of eight machine learning algorithms across ten clinical metabolomics data sets for binary classification.
Article in Metabolomics : Official journal of the Metabolomic Society, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 89 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
89 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Periodontal disease metabolomics signatures from different biofluids: a systematic review.Metabolomics : Official journal of the Metabolomic Society · 2022Pooled it
- Insufficient sleep and weekend recovery sleep: classification by a metabolomics-based machine learning ensemble.Scientific reports · 2023Trial
- Lipidomics study of plasma from patients suggest that ALS and PLS are part of a continuum of motor neuron disorders.Scientific reports · 2021Trial
- Integration of artificial intelligence and multi-omics for precision medicine.Functional & integrative genomics · 2026Review
- ENVnet provides a global molecular resource of dissolved organic matter.Nature biotechnology · 2026Article
- A Reporting Guideline for Observational Studies in Metabolomic Epidemiology: Explanation and Elaboration of the Strobe-MetEpi Checklist.Research square · 2026Article
- Modeling and optimization of methylene blue adsorption on biochar via artificial neural network (ANN).Biodegradation · 2026Article
- Construction and validation of explainable machine learning models to predict in-hospital mortality for patients with acute type A aortic dissection surgery.Frontiers in medicine · 2026Article
- Chemometric Methods Applied to Infrared and Raman Spectroscopy for Arboviruses Diagnosis: A Systematic Review With Meta-Analysis.Analytical science advances · 2025Review
- PSM-SMOTE: propensity score matching and synthetic minority oversampling for handling unbalanced microbiome data.Genes & genomics · 2025Article
- Solid-phase microextraction of sweat components of patients positive for Sars-Cov-2 for identification of possible biomarkers.Scientific reports · 2025Article
- Identification of biomarkers in gynecologic cancers: a machine learning approach for metabolomics.Translational and clinical pharmacology · 2025Article
- Machine Learning-Driven Insights in Cancer Metabolomics: From Subtyping to Biomarker Discovery and Prognostic Modeling.Metabolites · 2025Review
- On Selecting Robust Approaches for Learning Predictive Biomarkers in Metabolomics Data Sets.Analytical chemistry · 2025Article
- Recent Advances in Liquid Chromatography-Mass Spectrometry (LC-MS) Applications in Biological and Applied Sciences.Analytical science advances · 2025Review
- The role of alpha-synuclein in synucleinopathy: Impact on lipid regulation at mitochondria-ER membranes.NPJ Parkinson's disease · 2025Article
- A Comparative Study of Network-Based Machine Learning Approaches for Binary Classification in Metabolomics.Metabolites · 2025Article
- ESPClust: unsupervised identification of modifiers for the effect size profile in omics association studies.Bioinformatics (Oxford, England) · 2025Article
- A Comprehensive Machine Learning Approach for COVID-19 Target Discovery in the Small-Molecule Metabolome.Metabolites · 2025Article
- From Spectra to Signatures: Detecting Fentanyl in Human Nails with ATR-FTIR and Machine Learning.Sensors (Basel, Switzerland) · 2025Article
29 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionMetabolomics is increasingly being used in the clinical setting for disease diagnosis, prognosis and risk prediction. Machine learning algorithms are particularly important in the construction of multivariate metabolite prediction. Historically, partial least squares (PLS) regression has been the gold standard for binary classification. Nonlinear machine learning methods such as random forests (RF), kernel support vector machines (SVM) and artificial neural networks (ANN) may be more suited to modelling possible nonlinear metabolite covariance, and thus provide better predictive models.
objectivesWe hypothesise that for binary classification using metabolomics data, non-linear machine learning methods will provide superior generalised predictive ability when compared to linear alternatives, in particular when compared with the current gold standard PLS discriminant analysis.
methodsWe compared the general predictive performance of eight archetypal machine learning algorithms across ten publicly available clinical metabolomics data sets. The algorithms were implemented in the Python programming language. All code and results have been made publicly available as Jupyter notebooks.
resultsThere was only marginal improvement in predictive ability for SVM and ANN over PLS across all data sets. RF performance was comparatively poor. The use of out-of-bag bootstrap confidence intervals provided a measure of uncertainty of model prediction such that the quality of metabolomics data was observed to be a bigger influence on generalised performance than model choice.
conclusionThe size of the data set, and choice of performance metric, had a greater influence on generalised predictive performance than the choice of machine learning algorithm.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.