ReviewMetabolomics : Official journal of the Metabolomic Society2019
The application of artificial neural networks in metabolomics: a historical perspective.
Review 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 37 papers.
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Who cites it
37 citing papers in PubMed.
- Cultivar to chemotype: characterizing complex botanicals with mass spectrometry metabolomics.Natural product reports · 2026Review
- Ambient high-spatial-resolution mass spectrometry imaging: recent advances in technologies and metabolomics studies.Trends in analytical chemistry : TRAC · 2026Article
- Artificial Intelligence-Based Methods and Omics for Mental Illness Diagnosis: A Review.Bioengineering (Basel, Switzerland) · 2025Review
- Revolutionizing multi-omics analysis with artificial intelligence and data processing.Quantitative biology (Beijing, China) · 2025Review
- Recent Advances in Liquid Chromatography-Mass Spectrometry (LC-MS) Applications in Biological and Applied Sciences.Analytical science advances · 2025Review
- Artificial neural network detection of pancreatic cancer from proton (1H) magnetic resonance spectroscopy patterns of plasma metabolites.Communications medicine · 2025Article
- Dietary fat and carbohydrate-balancing the lactation performance and methane emissions in the dairy cow industry: A meta-analysis.Animal nutrition (Zhongguo xu mu shou yi xue hui) · 2024Article
- Predictive Prognostic Factors in Non-Calcific Supraspinatus Tendinopathy Treated with Focused Extracorporeal Shock Wave Therapy: An Artificial Neural Network Approach.Life (Basel, Switzerland) · 2024Article
- Can feature structure improve model's precision? A novel prediction method using artificial image and image identification.JAMIA open · 2024Article
- Article
- Deep-Learning-Based Mixture Identification for Nuclear Magnetic Resonance Spectroscopy Applied to Plant Flavors.Molecules (Basel, Switzerland) · 2023Article
- Opening the Random Forest Black Box ofMetabolites · 2023Article
- Metabolomics: A useful tool for ischemic stroke research.Journal of pharmaceutical analysis · 2023Review
- Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism.Current opinion in chemical biology · 2023Review
- Rise of Deep Learning Clinical Applications and Challenges in Omics Data: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Wide and deep learning based approaches for classification of Alzheimer's disease using genome-wide association studies.PloS one · 2023Article
- Development of Non-Targeted Mass Spectrometry Method for Distinguishing Spelt and Wheat.Foods (Basel, Switzerland) · 2022Article
- The metabolomics of human aging: Advances, challenges, and opportunities.Science advances · 2022Review
- Learning a confidence score and the latent space of a new supervised autoencoder for diagnosis and prognosis in clinical metabolomic studies.BMC bioinformatics · 2022Article
- Diagnosis and prognosis of COVID-19 employing analysis of patients' plasma and serum via LC-MS and machine learning.Computers in biology and medicine · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMetabolomics data, with its complex covariance structure, is typically modelled by projection-based machine learning (ML) methods such as partial least squares (PLS) regression, which project data into a latent structure. Biological data are often non-linear, so it is reasonable to hypothesize that metabolomics data may also have a non-linear latent structure, which in turn would be best modelled using non-linear equations. A non-linear ML method with a similar projection equation structure to PLS is artificial neural networks (ANNs). While ANNs were first applied to metabolic profiling data in the 1990s, the lack of community acceptance combined with limitations in computational capacity and the lack of volume of data for robust non-linear model optimisation inhibited their widespread use. Due to recent advances in computational power, modelling improvements, community acceptance, and the more demanding needs for data science, ANNs have made a recent resurgence in interest across research communities, including a small yet growing usage in metabolomics. As metabolomics experiments become more complex and start to be integrated with other omics data, there is potential for ANNs to become a viable alternative to linear projection methods. AIM OF REVIEW: We aim to first describe ANNs and their structural equivalence to linear projection-based methods, including PLS regression. We then review the historical, current, and future uses of ANNs in the field of metabolomics. KEY SCIENTIFIC CONCEPT OF REVIEW: Is metabolomics ready for the return of artificial neural networks?
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