Artificial Intelligence for Plant Gene Regulatory Networks
- 1Sainsbury Laboratory, University of Cambridge, Bateman Street, Cambridge CB2 1LR, UK
- 2Department of Cell and Developmental Biology, John Innes Centre, Norwich Research Park, Norwich NR4 7UH, UK
Abstract
Gene regulatory networks (GRNs) are the maps that plant biologists most want to draw, with arrows pointing from transcription factors to the genes they control, explaining how plants develop and respond to the environment. Since experimental validation of even one arrow can take years, and a single plant genome harbours tens of millions of candidate TF–target pairs, this gap has driven the development of numerous computational methods for GRN inference. Here I review three generations of computational approaches to plant GRN inference and measure them against a five-level ladder of evidence, including statistical association, conditional dependence, physical binding, functional regulation and causal circuits. Classical statistics and machine learning have simplified complex gene expression data into clear, prioritised predictions. Techniques like single-cell and multi-omics methods help uncover cell type-specific networks based on chromatin accessibility and binding motifs. Now, deep learning can interpret cis-regulatory grammar directly from genome sequences, and foundation models trained on plant genomes and millions of cells hold exciting potential to share regulatory insights across different species. The most promising route forward is a lab-in-the-loop division of labour in which foundation models serve not as oracles but as hypothesis engines, collapsing fifty million candidate TF–target pairs into the few hundred edges most worth the bench’s time, with each round of validation sharpening the next round of prediction.
Related
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BibTeX
@article{yu2026artificial,
title = {{Artificial Intelligence for Plant Gene Regulatory Networks}},
author = {Haopeng Yu},
journal = {International journal of molecular sciences},
year = {2026},
month = oct,
doi = {10.3390/ijms27198819},
url = {https://europepmc.org/article/PMC/PMC13664937},
note = {CC BY},
}
RIS
TY - JOUR
TI - Artificial Intelligence for Plant Gene Regulatory Networks
AU - Haopeng Yu
PY - 2026
DA - 2026/10/01
JO - International journal of molecular sciences
DO - 10.3390/ijms27198819
UR - https://europepmc.org/article/PMC/PMC13664937
AB - Gene regulatory networks (GRNs) are the maps that plant biologists most want to draw, with arrows pointing from transcription factors to the genes they control, explaining how plants develop and respond to the environment. Since experimental validation of even one arrow can take years, and a single plant genome harbours tens of millions of candidate TF–target pairs, this gap has driven the development of numerous computational methods for GRN inference. Here I review three generations of computational approaches to plant GRN inference and measure them against a five-level ladder of evidence, including statistical association, conditional dependence, physical binding, functional regulation and causal circuits. Classical statistics and machine learning have simplified complex gene expression data into clear, prioritised predictions. Techniques like single-cell and multi-omics methods help uncover cell type-specific networks based on chromatin accessibility and binding motifs. Now, deep learning can interpret cis-regulatory grammar directly from genome sequences, and foundation models trained on plant genomes and millions of cells hold exciting potential to share regulatory insights across different species. The most promising route forward is a lab-in-the-loop division of labour in which foundation models serve not as oracles but as hypothesis engines, collapsing fifty million candidate TF–target pairs into the few hundred edges most worth the bench’s time, with each round of validation sharpening the next round of prediction.
SN - 1422-0067
ER -