Abstract
Metabolomics has emerged as a promising field in pharmaceuticals and preventive healthcare, offering practical applications in disease detection and drug testing. However, the analysis and interpretation of complex metabolic datasets remain challenging, with current methods relying heavily on limited and incompletely annotated biological pathways. To overcome these limitations, we propose a novel approach that involves training machine learning classifiers on fingerprint-based encodings of metabolites to predict their response under specific experimental conditions. In this study, we evaluate our approach using a cellular model for the genetic disease Ataxia Telangiectasia (AT). Remarkably, some of our trained models predict affected metabolites with good performance, providing compelling evidence that the structural properties of metabolites hold predictive power over their response to specific conditions. Additionally, we suggest that evaluating the feature importance of the model can greatly assist researchers in identifying clusters of significant molecules and formulating hypotheses about affected pathways. Notably, our analysis of the AT cellular model identifies distinct groups of metabolites, some of which were already known to participate in the affected pathways, thereby validating existing knowledge. Moreover, we discovered metabolites not previously associated with AT, opening up novel opportunities for further exploration.
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This work has been funded by the European Union - NextGenerationEU under the Italian Ministry of University and Research (MUR) National Innovation Ecosystem grant ECS00000041 - VITALITY - CUP H33C22000430006.
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Sirocchi, C. et al. (2025). Molecular Fingerprints-Based Machine Learning for Metabolic Profiling. In: Meo, R., Silvestri, F. (eds) Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2023. Communications in Computer and Information Science, vol 2136. Springer, Cham. https://doi.org/10.1007/978-3-031-74640-6_8
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