ArticleMetabolomics : Official journal of the Metabolomic Society2020
Migrating from partial least squares discriminant analysis to artificial neural networks: a comparison of functionally equivalent visualisation and feature contribution tools using jupyter notebooks.
Article in Metabolomics : Official journal of the Metabolomic Society, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 52 citations in OpenAlex.
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- A new statistical workflow (R-packages based) to investigate associations between one variable of interest and the metabolome.Metabolomics : Official journal of the Metabolomic Society · 2023Article
- Cardioprotective Mechanism of Leonurine against Myocardial Ischemia through a Liver-Cardiac Crosstalk Metabolomics Study.Biomolecules · 2022Article
- Multi-Omics Integration in a Twin Cohort and Predictive Modeling of Blood Pressure Values.Omics : a journal of integrative biology · 2022Article
- The Impact of the hAPP695SW Transgene and Associated Amyloid-β Accumulation on Murine Hippocampal Biochemical Pathways.Journal of Alzheimer's disease : JAD · 2022Article
- Longitudinal metabolomics of human plasma reveals prognostic markers of COVID-19 disease severity.Cell reports. Medicine · 2021Article
- Discrepancies in metabolomic biomarker identification from patient-derived lung cancer revealed by combined variation in data pre-treatment and imputation methods.Metabolomics : Official journal of the Metabolomic Society · 2021Article
- Coupling Machine Learning and Lipidomics as a Tool to Investigate Metabolic Dysfunction-Associated Fatty Liver Disease. A General Overview.Biomolecules · 2021Review
- Approaches to Integrating Metabolomics and Multi-Omics Data: A Primer.Metabolites · 2021Review
- Longitudinal Metabolomics of Human Plasma Reveals Robust Prognostic Markers of COVID-19 Disease Severity.medRxiv : the preprint server for health sciences · 2021Article
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Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionMetabolomics data is commonly modelled multivariately using partial least squares discriminant analysis (PLS-DA). Its success is primarily due to ease of interpretation, through projection to latent structures, and transparent assessment of feature importance using regression coefficients and Variable Importance in Projection scores. In recent years several non-linear machine learning (ML) methods have grown in popularity but with limited uptake essentially due to convoluted optimisation and interpretation. Artificial neural networks (ANNs) are a non-linear projection-based ML method that share a structural equivalence with PLS, and as such should be amenable to equivalent optimisation and interpretation methods.
objectivesWe hypothesise that standardised optimisation, visualisation, evaluation and statistical inference techniques commonly used by metabolomics researchers for PLS-DA can be migrated to a non-linear, single hidden layer, ANN.
methodsWe compared a standardised optimisation, visualisation, evaluation and statistical inference techniques workflow for PLS with the proposed ANN workflow. Both workflows were implemented in the Python programming language. All code and results have been made publicly available as Jupyter notebooks on GitHub.
resultsThe migration of the PLS workflow to a non-linear, single hidden layer, ANN was successful. There was a similarity in significant metabolites determined using PLS model coefficients and ANN Connection Weight Approach.
conclusionWe have shown that it is possible to migrate the standardised PLS-DA workflow to simple non-linear ANNs. This result opens the door for more widespread use and to the investigation of transparent interpretation of more complex ANN architectures.
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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.