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Record type:
stať ve sborníku (D)
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Katedra informatiky a počítačů (31400)
Title:
Metabolomic Predictions via SOM: A Cold-Stress Case Study in Arabidopsis thaliana
Citace
Schwarzerova, J., Volná, E., Waldherr, S., Provazník, V. a Weckwerth, W. Metabolomic Predictions via SOM: A Cold-Stress Case Study in Arabidopsis thaliana.
In:
IWBBIO 2025: International Work-Conference on Bioinformatics and Biomedical Engineering: Bioinformatics and Biomedical Engineering 2025-07-16 Gran Canaria.
Cham: Springer, 2025. s. 322-333. ISBN 978-3-032-08452-1.
Subtitle
Publication year:
2025
Obor:
Number of pages:
12
Page from:
322
Page to:
333
Form of publication:
Elektronická verze
ISBN code:
978-3-032-08452-1
ISSN code:
Proceedings title:
Bioinformatics and Biomedical Engineering
Proceedings:
Mezinárodní
Publisher name:
Springer
Place of publishing:
Cham
Country of Publication:
Sborník vydaný v zahraničí
Název konference:
IWBBIO 2025: International Work-Conference on Bioinformatics and Biomedical Engineering
Conference venue:
Gran Canaria
Datum zahájení konference:
Typ akce podle státní
příslušnosti účastníků:
Evropská akce
WoS code:
EID:
2-s2.0-105022894153
Key words in English:
Metabolomic Predictions, Self-Organizing Maps, Arabidopsis thaliana
Annotation in original language:
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.
Annotation in english language:
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.
References
Reference
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