Orlenko, A;
Kofink, D;
Lyytikäinen, L-P;
Nikus, K;
Mishra, P;
Kuukasjärvi, P;
Karhunen, PJ;
... Moore, JH; + view all
(2020)
Model selection for metabolomics: predicting diagnosis of coronary artery disease using automated machine learning.
Bioinformatics
, 36
(6)
pp. 1772-1778.
10.1093/bioinformatics/btz796.
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Abstract
MOTIVATION: Selecting 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). RESULTS: We 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.
| Type: | Article |
|---|---|
| Title: | Model selection for metabolomics: predicting diagnosis of coronary artery disease using automated machine learning |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/bioinformatics/btz796 |
| Publisher version: | https://doi.org/10.1093/bioinformatics/btz796 |
| Language: | English |
| Additional information: | Copyright © The Author(s) 2019. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10094940 |
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