ArticleProceedings of the National Academy of Sciences of the United States of America2021
Predicting transcriptional responses to cold stress across plant species.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
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Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Integration of meta-analysis, machine learning and systems biology approach for investigating the transcriptomic response to drought stress in Populus species.Scientific reports · 2023Pooled it
- Widespread turnover of a conserved cis-regulatory code across 589 grass species.Molecular biology and evolution · 2026Article
- Whole genome resequencing analysis of tobacco K326 and its cold sensitive mutant M18.Frontiers in plant science · 2026Article
- Gene Expression Profiling of Transcription Factors and Acclimation-Related Genes inInternational journal of molecular sciences · 2025Article
- Predicting Gene Expression Responses to Cold inGenes · 2025Article
- Brassinosteroids Enhance Low-Temperature Resistance by Promoting the Formation of Sugars in Maize Mesocotyls.Plants (Basel, Switzerland) · 2025Article
- Machine learning models highlight environmental and genetic factors associated with the Arabidopsis circadian clock.Nature communications · 2025Article
- Designing a nitrogen-efficient cold-tolerant maize for modern agricultural systems.The Plant cell · 2025Review
- Machine Learning-Based identification of resistance genes associated with sunflower broomrape.Plant methods · 2025Article
- Exogenous 24-Epibrassinolide Improves Low-Temperature Tolerance of Maize Seedlings by Influencing Sugar Signaling and Metabolism.International journal of molecular sciences · 2025Article
- Identification of lineage-specific cis-trans regulatory networks related to kiwifruit ripening initiation.The Plant journal : for cell and molecular biology · 2024Article
- Rhythmic lipid and gene expression responses to chilling in panicoid grasses.Journal of experimental botany · 2024Article
- Organ-delimited gene regulatory networks provide high accuracy in candidate transcription factor selection across diverse processes.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Machine learning assists prediction of genes responsible for plant specialized metabolite biosynthesis by integrating multi-omics data.BMC genomics · 2024Article
- Machine Learning Application in Horticulture and Prospects for Predicting Fresh Produce Losses and Waste: A Review.Plants (Basel, Switzerland) · 2024Review
- Genome-wide identification and analysis of theFrontiers in plant science · 2024Article
- A review of artificial intelligence-assisted omics techniques in plant defense: current trends and future directions.Frontiers in plant science · 2024Review
- Genome-Wide Characterization and Expression Analysis of Transcription Factor Families in Desert MossInternational journal of molecular sciences · 2023Article
- Advances of Apetala2/Ethylene Response Factors in Regulating Development and Stress Response in Maize.International journal of molecular sciences · 2023Review
- DNABERT-based explainable lncRNA identification in plant genome assemblies.Computational and structural biotechnology journal · 2023Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although genome-sequence assemblies are available for a growing number of plant species, gene-expression responses to stimuli have been cataloged for only a subset of these species. Many genes show altered transcription patterns in response to abiotic stresses. However, orthologous genes in related species often exhibit different responses to a given stress. Accordingly, data on the regulation of gene expression in one species are not reliable predictors of orthologous gene responses in a related species. Here, we trained a supervised classification model to identify genes that transcriptionally respond to cold stress. A model trained with only features calculated directly from genome assemblies exhibited only modest decreases in performance relative to models trained by using genomic, chromatin, and evolution/diversity features. Models trained with data from one species successfully predicted which genes would respond to cold stress in other related species. Cross-species predictions remained accurate when training was performed in cold-sensitive species and predictions were performed in cold-tolerant species and vice versa. Models trained with data on gene expression in multiple species provided at least equivalent performance to models trained and tested in a single species and outperformed single-species models in cross-species prediction. These results suggest that classifiers trained on stress data from well-studied species may suffice for predicting gene-expression patterns in related, less-studied species with sequenced genomes.
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