ArticleNature communications2024
Deep learning the cis-regulatory code for gene expression in selected model plants.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
- Translating functional molecular knowledge into crop-breeding success.Nature reviews. Genetics · 2026Review
- Predicting transcriptional regulators in plants in the era of artificial intelligence.Current opinion in plant biology · 2026Review
- Validation of the International Weed Genomics Consortium genome annotation pipeline through reannotation of the model species Arabidopsis thaliana.The plant genome · 2026Article
- Deep Learning for Deciphering the Plant Cis-Regulatory Code.Plants (Basel, Switzerland) · 2026Review
- Conditionally Significant eQTL and TWAS Analyses Identify MsRD26 as a Major Candidate Positive Regulator of Salt Tolerance in Medicago sativa L.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A deep learning model captures position-specific effects of plant regulatory sequences and suggests genes under complex regulation.Plant physiology · 2026Article
- Harnessing polyploidy for climate-resilient crops: Lessons from the evolutionary model, allotetraploid cotton.Proceedings of the National Academy of Sciences of the United States of America · 2026Review
- Application of deep learning in crop research: From genomics to phenomics.The plant genome · 2026Review
- Transcription factor HvDREB4.1 and HvDREB4.2 from Hulless barley enhance tolerance to drought and salt stress in transgenic Arabidopsis thaliana.BMC plant biology · 2026Article
- PlantSetDelta: Mining regulatory features to enable interpretable gene set analysis in plants.Plant communications · 2026Article
- Chromatin accessibility landscape and its association with heterosis in maize hybrids.Nature communications · 2026Article
- Design and Testing of Root-Specific Synthetic Promoters by Machine Learning.International journal of molecular sciences · 2026Article
- From Natural Discovery to AI-Guided Design: A Curated Collection of Compact Enhancers for Crop Engineering.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Synthetic Biology of Plants and Microbes for Agriculture, Environment, and Future Applications.Chemical reviews · 2026Review
- Printing technologies for monitoring crop health.Nature communications · 2026Review
- Epigenome and interactome profiling uncovers principles of distal regulation in the barley genome.Cell genomics · 2026Article
- Cross-species prediction of histone modifications in plants via deep learning.Genome biology · 2026Article
- From reference-genome prediction to causal generalization: sequence-to-function models for plant regulatory genomics.Frontiers in plant science · 2026Review
- Synthetic promoter design in plants: integration of computational and experimental approaches.Frontiers in plant science · 2026Review
- AI-assisted crop improvement: new design tools within established regulatory frameworks.Frontiers in plant science · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Elucidating the relationship between non-coding regulatory element sequences and gene expression is crucial for understanding gene regulation and genetic variation. We explored this link with the training of interpretable deep learning models predicting gene expression profiles from gene flanking regions of the plant species Arabidopsis thaliana, Solanum lycopersicum, Sorghum bicolor, and Zea mays. With over 80% accuracy, our models enabled predictive feature selection, highlighting e.g. the significant role of UTR regions in determining gene expression levels. The models demonstrated remarkable cross-species performance, effectively identifying both conserved and species-specific regulatory sequence features and their predictive power for gene expression. We illustrated the application of our approach by revealing causal links between genetic variation and gene expression changes across fourteen tomato genomes. Lastly, our models efficiently predicted genotype-specific expression of key functional gene groups, exemplified by underscoring known phenotypic and metabolic differences between Solanum lycopersicum and its wild, drought-resistant relative, Solanum pennellii.
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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.