ArticleBioinformatics (Oxford, England)2020
Model selection for metabolomics: predicting diagnosis of coronary artery disease using automated machine learning.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed, 62 citations in OpenAlex.
- A machine learning approach to conglomerate multi-domain features of cardiac aging.European heart journal. Digital health · 2026Article
- Artificial intelligence to investigate metabolomics data for precision medicine.Metabolomics : Official journal of the Metabolomic Society · 2026Review
- The tree-based pipeline optimization tool: Tackling biomedical research problems with genetic programming and automated machine learning.Patterns (New York, N.Y.) · 2025Article
- Artificial intelligence in healthcare: a scoping review of perceived threats to patient rights and safety.Archives of public health = Archives belges de sante publique · 2024Article
- CrossMagma (New York, N.Y.) · 2024Article
- A Data-Driven Analysis of Ward Capacity Strain Metrics That Predict Clinical Outcomes Among Survivors of Acute Respiratory Failure.Journal of medical systems · 2023Article
- Automated quantitative trait locus analysis (AutoQTL).BioData mining · 2023Article
- Article
- Correlation between plasma glutathione peroxidase 4 and N-acetylneuraminic acid levels with clinical risk stratification and prognosis of patients with acute coronary syndrome.Saudi medical journal · 2022Article
- Precision Medicine Approaches with Metabolomics and Artificial Intelligence.International journal of molecular sciences · 2022Review
- What makes a good prediction? Feature importance and beginning to open the black box of machine learning in genetics.Human genetics · 2022Review
- The promise of automated machine learning for the genetic analysis of complex traits.Human genetics · 2022Review
- Machine Learning Algorithm to Predict Obstructive Coronary Artery Disease: Insights from the CorLipid Trial.Metabolites · 2022Article
- Automated Classification of Atherosclerotic Radiomics Features in Coronary Computed Tomography Angiography (CCTA).Diagnostics (Basel, Switzerland) · 2022Article
- Use of automated artificial intelligence to predict the need for orthodontic extractions.Korean journal of orthodontics · 2022Article
- Multi-constraints based deep learning model for automated segmentation and diagnosis of coronary artery disease in X-ray angiographic images.PeerJ. Computer science · 2022Article
- Image-Based Differentiation of Intracranial Metastasis From Glioblastoma Using Automated Machine Learning.Frontiers in neuroscience · 2022Article
- Deep Learning-Based Multi-Omics Integration Robustly Predicts Relapse in Prostate Cancer.Frontiers in oncology · 2022Article
- Hyperparameter Tuning and Pipeline Optimization via Grid Search Method and Tree-Based AutoML in Breast Cancer Prediction.Journal of personalized medicine · 2021Article
- Plasma-metabolite-based machine learning is a promising diagnostic approach for esophageal squamous cell carcinoma investigation.Journal of pharmaceutical analysis · 2021Article
Corrections and comments
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Authors and funding
12 authors at 4 institutions in 4 countries.
Funding
Abstract
motivationSelecting the optimal machine learning (ML) model for a given dataset is often challenging. Automated ML (AutoML) has emerged as a powerful tool for enabling the automatic selection of ML methods and parameter settings for the prediction of biomedical endpoints. Here, we apply the tree-based pipeline optimization tool (TPOT) to predict angiographic diagnoses of coronary artery disease (CAD). With TPOT, ML models are represented as expression trees and optimal pipelines discovered using a stochastic search method called genetic programing. We provide some guidelines for TPOT-based ML pipeline selection and optimization-based on various clinical phenotypes and high-throughput metabolic profiles in the Angiography and Genes Study (ANGES).
resultsWe analyzed nuclear magnetic resonance-derived lipoprotein and metabolite profiles in the ANGES cohort with a goal to identify the role of non-obstructive CAD patients in CAD diagnostics. We performed a comparative analysis of TPOT-generated ML pipelines with selected ML classifiers, optimized with a grid search approach, applied to two phenotypic CAD profiles. As a result, TPOT-generated ML pipelines that outperformed grid search optimized models across multiple performance metrics including balanced accuracy and area under the precision-recall curve. With the selected models, we demonstrated that the phenotypic profile that distinguishes non-obstructive CAD patients from no CAD patients is associated with higher precision, suggesting a discrepancy in the underlying processes between these phenotypes. AVAILABILITY AND IMPLEMENTATION: TPOT is freely available via http://epistasislab.github.io/tpot/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.