Metabolomic Predictions via SOM
- Author(s)
- Jana Schwarzerova, Eva Volna, Steffen Waldherr, Valentyna Provaznik, Wolfram Weckwerth
- Abstract
Understanding how Arabidopsis thaliana responds to cold stress at the metabolomic level is essential for uncovering plant resilience mechanisms. In this study, we applied Self-Organizing Maps (SOMs) for metabolomic prediction and pattern recognition. The dataset includes metabolite concentration values and realistic growth rates for 241 A. thaliana ecotypes, with each ecotype analyzed for 37 primary metabolites. These metabolites, particularly sugars, show significant concentration shifts in response to stress, making them ideal for detecting concept drift and understanding its impact on plant growth under cold stress conditions. The study utilized two distinct datasets: one from plants grown under standard growth conditions at 16 ℃, and the other from plants exposed to cold stress at 6 ℃. By applying SOMs to these data, we aimed to uncover patterns and predictive insights into the metabolomic changes induced by cold stress, providing new perspectives on the adaptive mechanisms of A. thaliana.
- Organisation(s)
- Functional and Evolutionary Ecology
- External organisation(s)
- Department of Molecular and Clinical Pathology and Medical Genetics, Department of Molecular and Clinical Pathology and Medical Genetics, University Hospital Ostrava, Ostrava, Czechia., Brno University of Technology, Department of Informatics and Computers, University of Ostrava, Czech Institute of Informatics, Robotics and Cybernetics
- Pages
- 322-333
- No. of pages
- 12
- DOI
- https://doi.org/10.1007/978-3-032-08452-1_26
- Publication date
- 11-2025
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 102004 Bioinformatics, 101028 Mathematical modelling, 106031 Plant physiology
- Keywords
- ASJC Scopus subject areas
- Theoretical Computer Science, General Computer Science
- Portal url
- https://ucrisportal.univie.ac.at/en/publications/854467fc-ba39-4f3b-920f-327056f05793