ArticleMolecular omics2018
Combined multivariate analysis and machine learning reveals a predictive module of metabolic stress response in Arabidopsis thaliana.
Article in Molecular omics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Insights into enhanced, divergent, and additive responses to single and combined hypoxia-salt stress.BMC plant biology · 2026Article
- Pan-omics insights into abiotic stress responses: bridging functional genomics and precision crop breeding.Functional & integrative genomics · 2025Review
- Bridging Ethnobotanical Knowledge and Multi-Omics Approaches for Plant-Derived Natural Product Discovery.Metabolites · 2025Review
- Millets for a sustainable future.Journal of experimental botany · 2025Review
- A review of artificial intelligence-assisted omics techniques in plant defense: current trends and future directions.Frontiers in plant science · 2024Review
- Advances in Plant Metabolomics and Its Applications in Stress and Single-Cell Biology.International journal of molecular sciences · 2022Review
- Machine Learning for Plant Stress Modeling: A Perspective towards Hormesis Management.Plants (Basel, Switzerland) · 2022Review
- Uncovering Pathways Highly Correlated to NUE through a Combined Metabolomics and Transcriptomics Approach in Eggplant.Plants (Basel, Switzerland) · 2022Article
- A Global Metabolic Map Defines the Effects of a Si-Based Biostimulant on Tomato Plants under Normal and Saline Conditions.Metabolites · 2021Article
- Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview.Metabolites · 2021Review
- Article
- Dynamics of Plant Metabolism during Cold Acclimation.International journal of molecular sciences · 2019Review
- The Power of LC-MS Based Multiomics: Exploring Adipogenic Differentiation of Human Mesenchymal Stem/Stromal Cells.Molecules (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Abiotic stress exposure of plants induces metabolic reprogramming which is tightly regulated by signalling cascades connecting transcriptional with translational and metabolic regulation. Complexity of such interconnected metabolic networks impedes the functional understanding of molecular plant stress response compromising the design of breeding strategies and biotechnological processes. Thus, defining a molecular network to enable the prediction of a plant's stress mode will improve the understanding of stress responsive biochemical regulation and will yield novel molecular targets for technological application. Arabidopsis wild type plants and two mutant lines with deficiency in sucrose or starch metabolism were grown under ambient and combined cold/high light stress conditions. Stress-induced dynamics of the primary metabolome and the proteome were quantified by mass spectrometry. Wild type data were used to train a machine learning algorithm to classify mutant lines under control and stress conditions. Multivariate analysis and classification identified a module consisting of 23 proteins enabling the reliable prediction of combined temperature/high light stress conditions. 18 of these 23 proteins displayed putative protein-protein interactions connecting transcriptional regulation with regulation of primary and secondary metabolism. The identified stress-responsive core module supports prediction of complex biochemical regulation under changing environmental conditions.
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