TY - GEN
T1 - Metabolomic Predictions via SOM
T2 - 12th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2025
AU - Schwarzerova, Jana
AU - Volna, Eva
AU - Waldherr, Steffen
AU - Provaznik, Valentyna
AU - Weckwerth, Wolfram
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
PY - 2026
Y1 - 2026
N2 - 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.
AB - 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.
KW - Arabidopsis thaliana
KW - Cold Stress
KW - Machine Learning
KW - Metabolomics
KW - Self-Organizing Maps
UR - https://www.scopus.com/pages/publications/105022894153
U2 - 10.1007/978-3-032-08452-1_26
DO - 10.1007/978-3-032-08452-1_26
M3 - Contribution to proceedings
AN - SCOPUS:105022894153
SN - 9783032084514
T3 - Lecture Notes in Computer Science
SP - 322
EP - 333
BT - Bioinformatics and Biomedical Engineering - 12th International Conference, IWBBIO 2025, Proceedings
A2 - Rojas, Ignacio
A2 - Ortuño, Francisco
A2 - Rojas Ruiz, Fernando
A2 - Herrera, Luis Javier
A2 - Escobar, Juan José
A2 - Valenzuela, Olga
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 16 July 2025 through 18 July 2025
ER -