ArticleFrontiers in plant science2023
Predicting transcriptional responses to heat and drought stress from genomic features using a machine learning approach in rice.
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.
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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.
- Genomics, Multi-Omics, and Emerging Artificial Intelligence for Combined Drought-Heat Stress Resilience in Plants: A Structured Narrative Review.Plants (Basel, Switzerland) · 2026Review
- Machine Learning-Guided Stress Atlases Reveal Co-Expression Rewiring and Divergent Cellular Deployment of Abiotic Stress Programs in Rice and Wheat.International journal of molecular sciences · 2026Article
- Applications of Gene-Editing Technologies in Enhancing Crop Stress Resistance with Emphasis on Rice.Plants (Basel, Switzerland) · 2026Review
- Recent Advances and Application of Machine Learning for Protein-Protein Interaction Prediction in Rice: Challenges and Future Perspectives.Proteomes · 2025Review
- Predicting Gene Expression Responses to Cold inGenes · 2025Article
- Gaining insights into epigenetic memories through artificial intelligence and omics science in plants.Journal of integrative plant biology · 2025Review
- Bisphenol A causes melatonin biosynthesis epigenetic reprogramming of melatonin biosynthesis genes in arabidopsis thaliana.Communications biology · 2025Article
- Using supervised machine-learning approaches to understand abiotic stress tolerance and design resilient crops.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Review
- Deep learning the cis-regulatory code for gene expression in selected model plants.Nature communications · 2024Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Plants have evolved various mechanisms to adapt to adverse environmental stresses, such as the modulation of gene expression. Expression of stress-responsive genes is controlled by specific regulators, including transcription factors (TFs), that bind to sequence-specific binding sites, representing key components of cis-regulatory elements and regulatory networks. Our understanding of the underlying regulatory code remains, however, incomplete. Recent studies have shown that, by training machine learning (ML) algorithms on genomic sequence features, it is possible to predict which genes will transcriptionally respond to a specific stress. By identifying the most important features for gene expression prediction, these trained ML models allow, in theory, to further elucidate the regulatory code underlying the transcriptional response to abiotic stress. Here, we trained random forest ML models to predict gene expression in rice (
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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.