Speaker
Description
SP
The Digital Signature of Metabolites: AI-Powered Biomarker Discovery
Metabolites are low–molecular-weight chemical compounds within cells, which together constitute the metabolome1. Metabolomic studies provide a near-real-time view of the phenotype. The resulting biomarker candidates show promise in personalized medicine, early detection, drug discovery and treatment-response monitoring.
However, metabolomic datasets are typically large and complex. Therefore artificial intelligence (AI) has become an essential tool to obtain reliable, generalizable digital signatures in the analysis of metabolite patterns that reflect various biological states2. As a branch of AI, machine learning facilitates detection of metabolic patterns, feature selection, visualization, and the extraction of non-linear relationships from various metabolomic datasets. Although the field of metabolomics and AI-driven biomarker discovery is rapidly growing, several challenges remain, including the high dimensionality of data, batch effects, and limited sample sizes3. These challenges underscore the importance of interdisciplinary collaboration across diverse fields.
This study outlines how AI-driven data mining plays a pivotal role in biomarker identification and in improving clinical relevance of metabolomics. We aim to provide a concise overview of emerging AI methodologies and improvements in metabolic applications enabled by AI-based data processing.
Keywords: metabolomics, artificial intelligence, digital signature, machine learning, biomarker
References
1. Kopeć, Karolina Krystyna, et al. Unlocking the secrets of metabolomics with Artificial Intelligence: A comprehensive literature review. Journal of Pediatric and Neonatal Individualized Medicine (JPNIM) 13.1, 2024, DOI: 10.7363/130105
2. Yetgin, Ali. Revolutionizing Multi‐Omics Analysis With Artificial Intelligence And Data Processing. Quantitative Biology 13.3, 2025, DOI: 10.1002/qub2.70002
3. Li, Rufeng, et al. Machine Learning Meets Omics: Applications And Perspectives. Briefings in Bioinformatics 23.1 2022, DOI: 10.1093/bib/bbab560
