ArticlePloS one2023
Interpretable machine learning with tree-based shapley additive explanations: Application to metabolomics datasets for binary classification.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers.
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Who cites it
60 citing papers in PubMed.
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- Prognostic prediction model for rectal cancer based on CMS subtype indicators and SHAP-based interpretable analysis.Abdominal radiology (New York) · 2026Article
- ML-BUSMetab: Machine Learning-Based Metabolomic Profiling for Predicting Aspirin Response in Colorectal Cancer Chemoprevention: A Multi-Model Explainable Artificial Intelligence Approach with External Validation.Journal of clinical medicine · 2026Article
- Applying Machine learning to analyze the utilization of insecticide-treated nets among rural under-five children in East Africa.Parasite epidemiology and control · 2026Review
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- Modeling Zero-Dose Children in Ethiopia: A Machine Learning Perspective on Model Performance and Predictor Variables.JMIR pediatrics and parenting · 2026Article
- Current Advancements in Untargeted Metabolomics Analysis and Testing Driven by Machine Learning: Prospects for Artificial Intelligence in Patient-Centric Healthcare Transformation.Applied biochemistry and biotechnology · 2026Review
- Epidemiological trends and risk factors of early-onset colorectal cancer in the founding BRICS countries, 1990-2023, with projections to 2050.Frontiers in oncology · 2026Article
- Rapid prediction of vancomycin-resistantFrontiers in microbiology · 2026Article
- The application of explainable machine learning for the identification of MASLD in non-obese populations.Frontiers in public health · 2026Article
- Machine Learning Models to Predict Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) With Simple Anthropometric and Biochemical Variables: A Cross-Sectional Study in US Population.International journal of hepatology · 2026Article
- Association Between the Lactate-to-Albumin Ratio and ICU/In-Hospital Mortality in Critically Ill Patients With Comorbid Type 2 Diabetes Mellitus : A Cohort Study Utilizing the MIMIC-IV Database.Emergency medicine international · 2026Article
- Creation and Verification of a Machine Learning-based Model for Predicting Breast Cancer Risk in Patients with BI-RADS 4 Breast Lesions.Current medical imaging · 2026Article
- Nutritional-metabolic risk stratification for delirium in surgical critically ill patients: joint associations of the triglyceride-glucose index and stress hyperglycemia ratio.Frontiers in nutrition · 2026Article
- Identification of common genes and biomarkers between Dermatomyositis and rheumatoid arthritis through integrated bioinformatics.PloS one · 2026Article
- Explainable machine learning for early detection ofFrontiers in cellular and infection microbiology · 2026Article
- Genomic evolution of SARS-CoV-2 delta variants pre- and post-omicron emergence using alignment-free machine learning models.PloS one · 2026Article
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Abstract
Machine learning (ML) models are used in clinical metabolomics studies most notably for biomarker discoveries, to identify metabolites that discriminate between a case and control group. To improve understanding of the underlying biomedical problem and to bolster confidence in these discoveries, model interpretability is germane. In metabolomics, partial least square discriminant analysis (PLS-DA) and its variants are widely used, partly due to the model's interpretability with the Variable Influence in Projection (VIP) scores, a global interpretable method. Herein, Tree-based Shapley Additive explanations (SHAP), an interpretable ML method grounded in game theory, was used to explain ML models with local explanation properties. In this study, ML experiments (binary classification) were conducted for three published metabolomics datasets using PLS-DA, random forests, gradient boosting, and extreme gradient boosting (XGBoost). Using one of the datasets, PLS-DA model was explained using VIP scores, while one of the best-performing models, a random forest model, was interpreted using Tree SHAP. The results show that SHAP has a more explanation depth than PLS-DA's VIP, making it a powerful method for rationalizing machine learning predictions from metabolomics studies.
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