ArticleNature communications2021
Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 1 of them a synthesis that pooled it.
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Who cites it
51 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Drought-responsive genes in tomato: meta-analysis of gene expression using machine learning.Scientific reports · 2023Pooled it
- Predicting transcriptional regulators in plants in the era of artificial intelligence.Current opinion in plant biology · 2026Review
- Systems-Level Developmental Reprogramming Under Waterlogging Stress in Cowpea Revealed by Integrated Phenotypic, Physiological, and Transcriptomic Analysis.Plant, cell & environment · 2026Article
- Allele-specific methylation ofScience advances · 2026Article
- Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- DNAwhisper: An Integrated Deep Learning Pyramidal Framework for Multi-Trait Genomic Prediction and Adaptive Marker Prioritisation.Plant biotechnology journal · 2026Article
- Uncovering the early and conserved molecular mechanisms of root nitrogen foraging in model and crops.BMC genomics · 2026Article
- Inferring genotype-phenotype maps using attention models.PNAS nexus · 2026Article
- Connecting links with code: AI in morphological evolution.Frontiers in bioinformatics · 2026Review
- Decoding nitrogen uptake efficiency in maize and sorghum: insights from comparative gene regulatory networks.The Plant journal : for cell and molecular biology · 2025Article
- Predicting natural variation in the yeast phenotypic landscape with machine learning.Molecular systems biology · 2025Article
- Towards smart agriculture: AI-driven prediction of key genes for revolutionizing crop breeding.Planta · 2025Review
- Machine learning models highlight environmental and genetic factors associated with the Arabidopsis circadian clock.Nature communications · 2025Article
- Translational insights into abiotic interactions: From Arabidopsis to crop plants.The Plant cell · 2025Review
- Conservation and divergence of regulatory architecture in nitrate-responsive plant gene circuits.The Plant cell · 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
- Model-to-crop conserved NUE Regulons enhance machine learning predictions of nitrogen use efficiency.The Plant cell · 2025Article
- Shining light on Arabidopsis regulatory networks integrating nitrogen use and photosynthesis.The Plant journal : for cell and molecular biology · 2025Article
- Millets for a sustainable future.Journal of experimental botany · 2025Review
- Efficient mutagenesis and genotyping of maize inbreds using biolistics, multiplex CRISPR/Cas9 editing, and Indel-Selective PCR.Plant methods · 2025Article
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Inferring phenotypic outcomes from genomic features is both a promise and challenge for systems biology. Using gene expression data to predict phenotypic outcomes, and functionally validating the genes with predictive powers are two challenges we address in this study. We applied an evolutionarily informed machine learning approach to predict phenotypes based on transcriptome responses shared both within and across species. Specifically, we exploited the phenotypic diversity in nitrogen use efficiency and evolutionarily conserved transcriptome responses to nitrogen treatments across Arabidopsis accessions and maize varieties. We demonstrate that using evolutionarily conserved nitrogen responsive genes is a biologically principled approach to reduce the feature dimensionality in machine learning that ultimately improved the predictive power of our gene-to-trait models. Further, we functionally validated seven candidate transcription factors with predictive power for NUE outcomes in Arabidopsis and one in maize. Moreover, application of our evolutionarily informed pipeline to other species including rice and mice models underscores its potential to uncover genes affecting any physiological or clinical traits of interest across biology, agriculture, or medicine.
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