Artificial Intelligence and Machine Learning in Metabolomics
A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Integrative Metabolomics".
Deadline for manuscript submissions: 31 January 2026 | Viewed by 2046
Special Issue Editor
Interests: metabolomics; plant biotechnology; natural products; plant biochemistry
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The broad study of small molecules in biological systems is called metabolomics. It uses high-tech analytical techniques like nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) to create massive datasets that necessitate analysis through very complex computer-based methods. This field encompasses critical tasks ranging from raw data processing, such as denoising and alignment, to feature detection, compound identification, biological model construction, and multi-omics data integration.
The integration of artificial intelligence (AI), machine learning (ML), and deep learning (DL) is revolutionizing metabolomics research. AI enables large-scale data analysis, pattern recognition, and metabolic pathway modeling, while ML drives predictive modeling, feature selection, and multi-omics integration. Deep learning techniques, such as convolutional neural networks (CNNs), excel in processing spectral and imaging data from MS and NMR, and generative adversarial networks (GANs) offer innovative solutions for synthetic data generation and augmentation. These technologies are transforming applications in biomarker discovery, metabolic network analysis, disease research, drug development, and personalized medicine.
This Special Issue invites contributions that highlight cutting-edge advancements in AI and ML for metabolomics. We welcome original research and reviews covering topics such as the following:
- AI/ML algorithms for metabolomic data analysis;
- Advanced pipelines for raw data processing, feature extraction, and compound identification;
- Integrative approaches for multi-omics data;
- Biomarkers and predictive models;
- Translational applications in clinical and pharmaceutical research.
Prof. Dr. Robert Winkler
Guest Editor
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 250 words) can be sent to the Editorial Office for assessment.
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
- artificial intelligence
- machine learning
- deep learning
- metabolomics
- integrative approaches
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