Machine Learning in Metabolomics: Unlocking the Future of Data Analysis
A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Bioinformatics and Data Analysis".
Deadline for manuscript submissions: 20 August 2025 | Viewed by 1157
Special Issue Editors
Interests: metabolomics; mass spectrometry; single-cell; maternal immune activation; CSF
Special Issues, Collections and Topics in MDPI journals
Interests: mass spectrometry; proteomics; metabolomics; multi-omics integration; bioinformatics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Machine learning (ML) is transforming the landscape of metabolomics, building on its revolutionary impact in precision medicine and other omics fields. With its ability to identify patterns and relationships in complex datasets without explicit programming, ML is driving advancements in biomarker discovery, disease and patient cohort classification, chemical composition analysis, and more.
A key breakthrough lies in the application of ML to compound annotation, where it has dramatically improved the identification of unknown small molecules. By refining fingerprinting techniques and introducing innovative algorithms, researchers are expanding the capabilities of untargeted metabolomics, uncovering new insights into the vast, unexplored universe of small molecules.
Another pivotal area is biomarker discovery. ML algorithms excel at processing large datasets, identifying specific metabolic signatures that indicate diseases. These discoveries hold enormous promise for precision medicine, enabling treatments tailored to an individual’s unique metabolic profile.
Moreover, ML is a cornerstone in multiomics integration—combining data from genomics, transcriptomics, proteomics, and metabolomics. This holistic approach provides a comprehensive view of biological systems, with ML enhancing both data interpretation and analytical precision.
However, despite significant advancements in analytical platforms and software, challenges remain in data processing and integration. These hurdles underscore the importance of developing novel ML methods tailored to the unique demands of metabolomics. As the field evolves, such innovations are critical for addressing data complexity, advancing our understanding of biological systems, and driving innovative personalized medicine solutions.
Dr. Boryana Petrova
Dr. Arzu Tugce Guler
Guest Editors
Manuscript Submission Information
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Keywords
- metabolomics
- MS imaging
- machine learning
- AI
- multi-omics
- integration
- biomarker discovery
- compound annotation
- spectra interpretation
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