Decoding gene regulation in plant genomes with artificial intelligence

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ID: 328193
2026
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Abstract
One of the central goals of plant functional genomics is to uncover regulatory mechanisms that shape agriculturally important traits to inform crop improvement. Recent advances in machine learning (ML) and artificial intelligence (AI), especially Large Language Models (LLMs), have greatly transformed our ability to derive regulatory information from complex genomics data. This review starts with a brief introduction of recent advances in AI and ML. We then present a plant-focused synthesis of emerging applications of AI- and LLM tools to: (i) predict epigenomic features, regulatory DNA elements, and gene expressions; (ii) infer gene regulatory network; and (iii) estimate post-transcriptional regulation.
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openalex_W7212114154 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Niraj Rayamajhi, Tianyang Xu, Tianci Liu, Jing Gao, Kranthi Varala, Ying Li
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Journal Journal of experimental botany
Year 2026
DOI
10.1093/jxb/erag323
URL
https://doi.org/10.1093/jxb/erag323
Keywords Keywords not found

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