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Metabolites
Special Issues
Machine Learning Applications in Metabolomics Analysis: 2nd Edition
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Machine Learning Applications in Metabolomics Analysis: 2nd Edition

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Bioinformatics and Data Analysis".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 649

Special Issue Editors

Prof. Dr. Leonardo Franco

E-Mail Website
Guest Editor
Department of Computer Science, Málaga University, 29071 Málaga, Spain
Interests: artificial intelligence; biomedicine; deep learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Martin Grootveld

E-Mail Website
Guest Editor
Leicester School of Pharmacy, Faculty of Health and Life Sciences, De Montfort University, Leicester LE1 9BH, UK
Interests: chemical pathology; clinical chemistry; NMR-based metabolomics; disease diagnosis and prognostic monitoring; metabolic pathway analysis; bioinorganic chemistry; drug design; development and synthesis; artificial intelligence; machine learning; research ethics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Metabolomics research is gaining much popularity since it enables the study of biological problems at a biochemical level, and can help us to understand the induction, development and mechanisms of many diseases, complementing information from other ‘omics technologies. Similar to other high-throughput biological technologies, metabolomics can produce large volumes of data, and therefore, machine learning strategies can facilitate its application, with the discovery of new biomolecular signatures, which consequently facilitate the diagnosis/prognostic monitoring of diseases, including rare metabolic disorders, etc.

This Special Issue aims to attract publications focused on the application of machine learning techniques to the analysis of multidimensional metabolomics data, including the development of methods, data augmentation procedures, preprocessing techniques, the comparisons of different methods, interpretability of results, the identification of new signatures, etc.

Prof. Dr. Leonardo Franco
Prof. Dr. Martin Grootveld
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metabolites is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • metabolites
  • biofluids
  • artificial intelligence
  • metabolomics
  • machine learning

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Published Papers (1 paper)

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Research

20 pages, 2540 KiB  
Article
Towards Optimizing Neural Network-Based Quantification for NMR Metabolomics
by Hayden Johnson and Aaryani Tipirneni-Sajja
Metabolites 2025, 15(4), 249; https://doi.org/10.3390/metabo15040249 - 4 Apr 2025
Viewed by 336
Abstract
Background: Quantification of metabolites from nuclear magnetic resonance (NMR) spectra in an accurate, high-throughput manner requires effective data processing tools. Neural networks are relatively underexplored in quantitative NMR metabolomics despite impressive speed and throughput compared to more conventional peak-fitting metabolomics software. Methods: This [...] Read more.
Background: Quantification of metabolites from nuclear magnetic resonance (NMR) spectra in an accurate, high-throughput manner requires effective data processing tools. Neural networks are relatively underexplored in quantitative NMR metabolomics despite impressive speed and throughput compared to more conventional peak-fitting metabolomics software. Methods: This work investigates practices for dataset and model development in the task of metabolite quantification directly from simulated NMR spectra for three neural network models: the multi-layered perceptron, the convolutional neural network, and the transformer. Model architectures, training parameters, and training datasets are optimized before comparing each model on simulated 400-MHz 1H-NMR spectra of complex mixtures with 8, 44, or 86 metabolites to quantify in spectra ranging from simple to highly complex and overlapping peaks. The optimized models were further validated on spectra at 100- and 800-MHz. Results: The transformer was the most effective network for NMR metabolite quantification, especially as the number of metabolites per spectra increased or target concentrations were low or had a large dynamic range. Further, the transformer was able to accurately quantify metabolites in simulated spectra from 100-MHz up to 800-MHz. Conclusions: The methods developed in this work reveal that transformers have the potential to accurately perform fully automated metabolite quantification in real-time and, with further development with experimental data, could be the basis for automated quantitative NMR metabolomics software. Full article
(This article belongs to the Special Issue Machine Learning Applications in Metabolomics Analysis: 2nd Edition)
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