Metabolomic Predictions via SOM: A Cold-Stress Case Study in Arabidopsis thaliana

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0200052" target="_blank" >RIV/00216305:26220/26:0200052 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-032-08452-1_26" target="_blank" >http://dx.doi.org/10.1007/978-3-032-08452-1_26</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-032-08452-1_26" target="_blank" >10.1007/978-3-032-08452-1_26</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Metabolomic Predictions via SOM: A Cold-Stress Case Study in Arabidopsis thaliana

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Metabolomic Predictions via SOM: A Cold-Stress Case Study in Arabidopsis thaliana

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Lecture Notes in Computer Science

ISBN

9783032084514

ISSN

—

e-ISSN

1611-3349

Počet stran výsledku

12

Strana od-do

322-333

Název nakladatele

Springer Science and Business Media Deutschland GmbH

Místo vydání

—

Místo konání akce

Canaria, Spain

Datum konání akce

16. 7. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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