ArticleFrontiers in plant science2023
Machine learning extracts marks of thiamine's role in cold acclimation in the transcriptome of
Article in Frontiers in plant science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed, 14 citations in OpenAlex.
- Decoding core molecular mechanisms of heat-stress tolerance in Brassica napus using transcriptomics and machine learning.Plant cell reports · 2026Article
- Annual transcriptomic trajectory of grapevine buds reveals the coordinated regulation of thiamine and stilbenoid biosynthesis during winter dormancy.BMC plant biology · 2026Article
- Integrative Physiological, Metabolomic and Transcriptomic Analyses Uncover the Mechanisms Underlying Differential Responses of TwoBiomolecules · 2025Article
- Responding to Stress: Diversity and Resilience of Grapevine in a Changing Climate Under the Perspective of Omics Research.International journal of molecular sciences · 2025Review
- Accelerating crop improvement via integration of transcriptome-based network biology and genome editing.Planta · 2025Review
- Time-course transcriptome analysis reveals gene co-expression networks and transposable element responses to cold stress in cotton.BMC genomics · 2025Article
- Multi-Omics Analysis Provides Insights into Green Soybean in Response to Cold Stress.Metabolites · 2024Article
- Unveiling Iso- and Aniso-Hydric Disparities in Grapevine-A Reanalysis by Transcriptome Portrayal Machine Learning.Plants (Basel, Switzerland) · 2024Article
- Special Issue on "Plant Biology and Biotechnology: Focus on Genomics and Bioinformatics 2.0".International journal of molecular sciences · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: The escalating challenge of climate change has underscored the critical need to understand cold defense mechanisms in cultivated grapevine Methods: We used Self Organizing Maps machine learning method to analyze gene expression data from leaves of five Results: Our analysis unveiled a connection with vitamin B1 (thiamine) biosynthesis, suggesting a link between temperature regulation and thiamine metabolism, in agreement with thiamine related stress response established in Arabidopsis before. Furthermore, we found that epigenetic mechanisms play a crucial role in regulating the expression of stress-responsive genes at low temperatures in grapevines. Discussion: Application of Self Organizing Maps portrayal to vine transcriptomics identified modules of coregulated genes triggered under cold stress. Our machine learning approach provides a promising option for transcriptomics studies in plants.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.