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Research on Artificial Intelligence in Plant Biology
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Research on Artificial Intelligence in Plant Biology

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Plant Sciences".

Deadline for manuscript submissions: closed (20 November 2025) | Viewed by 883

Special Issue Editor

Dr. Haopeng Yu

E-Mail Website
Guest Editor
Department of Cell and Developmental Biology, John Innes Centre, Norwich Research Park, Norwich, UK
Interests: machine learning; AI; RNA structure; post-transcription regulation; co-transcription regulation; translation; degradation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The complex “language” of nucleotides holds essential regulatory elements that guide plant growth, development, and adaptation. However, deciphering this genetic code remains a formidable challenge. Recent breakthroughs in artificial intelligence (AI) have enabled us to decode hidden patterns within DNA, RNA, and protein sequences, offering unprecedented insights into how plants regulate crucial biological functions. In particular, large language models—designed to process vast and diverse biological data—are now revealing subtle sequence and structural motifs that were previously difficult to identify. These advancements hold promise for addressing a variety of pressing issues in plant science, from optimizing crop yields to achieving sustainability under environmental stress.

This Special Issue aims to highlight innovative AI-driven research that illuminates the intricate regulatory elements within plant genomes and transcriptomes. We encourage submissions that explore novel computational methods, uncover functional motifs or “biological rules”, and integrate AI-based predictions with experimental validation. By showcasing both theoretical advances and practical applications, this collection seeks to drive forward the discovery and implementation of AI-powered strategies for sustainable agriculture, crop improvement, and beyond.

Dr. Haopeng Yu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • plant biology
  • RNA regulation
  • DNA regulatory elements
  • plant development
  • plant biotechnology

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Published Papers (1 paper)

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Review

42 pages, 2169 KB  
Review
Application of Artificial Intelligence Technology in Plant MicroRNA Research: Progress, Challenges, and Prospects
by Ruilin Yang and Hanma Zhang
Int. J. Mol. Sci. 2025, 26(24), 11854; https://doi.org/10.3390/ijms262411854 - 9 Dec 2025
Viewed by 141
Abstract
Plant microRNAs (miRNAs) are endogenous non-coding RNAs (~20–24 nucleotides) that regulate gene expression post-transcriptionally, playing critical roles in plant growth, development, and stress responses. This review systematically examines AI applications in plant miRNA research, tracing evolution from traditional machine learning to deep learning [...] Read more.
Plant microRNAs (miRNAs) are endogenous non-coding RNAs (~20–24 nucleotides) that regulate gene expression post-transcriptionally, playing critical roles in plant growth, development, and stress responses. This review systematically examines AI applications in plant miRNA research, tracing evolution from traditional machine learning to deep learning architectures. Plant miRNAs exhibit distinctive features necessitating plant-specific computational approaches: nuclear-localized biogenesis, high target complementarity (>80%), and coding region targeting. These characteristics enable more accurate computational prediction and experimental validation than animal systems. Methodological advances have improved prediction accuracy from ~90% (early SVMs) to >99% (recent deep learning), though metrics reflect different evaluation contexts. We analyze applications across miRNA identification, target prediction with degradome validation, miRNA–lncRNA interactions, and ceRNA networks. Critical assessment reveals that degradome data capture mixed RNA fragments from multiple sources beyond miRNA cleavage, requiring stringent multi-evidence validation. Similarly, fundamental ambiguities in lncRNA definition compound prediction uncertainties. Major challenges include severe data imbalance (positive to negative ratios of 1:100 to 1:10,000), limited cross-species generalization, insufficient model interpretability, and experimental validation bottlenecks. Approximately 75% of plant miRNA families in miRBase v20 lack convincing evidence, underscoring the need for rigorous annotation standards. Future directions encompass multimodal deep learning, explainable AI, spatiotemporal graph neural networks, and ultimately AI-driven de novo miRNA design, though the latter requires substantial advances in both computation and high-throughput validation. This synthesis demonstrates that AI has become indispensable for plant miRNA research, providing essential support for crop improvement while acknowledging persistent challenges demanding continued innovation. Full article
(This article belongs to the Special Issue Research on Artificial Intelligence in Plant Biology)
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