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Plants
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Computational Approaches to Decoding Plant Molecular Networks
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Computational Approaches to Decoding Plant Molecular Networks

A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Plant Molecular Biology".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 479

Special Issue Editor

Dr. Rajeev K. Azad

E-Mail Website
Guest Editor
1. Department of Biological Sciences, University of North Texas, Denton, TX 76203, USA
2. BioDiscovery Institute, University of North Texas, Denton, TX 76203, USA
3. Department of Mathematics, University of North Texas, Denton, TX 76203, USA
Interests: plants bioinformatics; computational genomics; genome evolution; pathogenomics; metagenomics; gene prediction; structural variation detection; disease gene identification
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid advancement of computational techniques has significantly enhanced the analysis of plant molecular data, providing crucial insights into the molecular mechanisms that govern plant growth, development, and responses to environmental changes. With the emergence of high-throughput technologies, vast amounts of plant molecular data, specifically from the fields of genomics, transcriptomics, proteomics, metabolomics, and epigenomics, are being generated at an unprecedented pace, presenting both challenges and opportunities for data analysis and interpretation.

This Special Issue aims to cover the latest research on the application of computational methods to plant molecular analyses. We welcome original research articles, review articles, and perspectives focusing on the development and application of advanced computational tools for plant molecular studies. Topics of interest include, but are not limited to, innovative approaches in multi-omics data processing, integration, and interpretation and the use of machine learning and artificial intelligence in plant biology. Additionally, we seek contributions that explore the visualization of complex plant molecular networks and spur new advances in understanding plant systems.

Dr. Rajeev K. Azad
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. Plants is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). 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

  • computational techniques
  • multi-omics data
  • plant molecular analysis
  • machine learning
  • AI
  • data integration

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Published Papers (1 paper)

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21 pages, 12110 KB  
Article
Deciphering Cell-Type-Specific Transcriptional Regulation in Tomato Leaves Through Ensemble Machine Learning and Single-Cell Transcriptomics
by Hui Shen, Wen Liu, Yuanheng Li, Zhaoyilan He, Zheng’an Yang, Zongli Hu and Ting Wu
Plants 2026, 15(10), 1578; https://doi.org/10.3390/plants15101578 - 21 May 2026
Viewed by 153
Abstract
High-throughput single-cell RNA sequencing (scRNA-seq) has substantially advanced plant transcriptional landscapes. However, decoding cell-type-specific transcriptional regulation in non-model crops like tomato (Solanum lycopersicum) remains challenging. An integrated computational pipeline was applied using high-dimensional weighted gene co-expression (hdWGCNA) and ensemble machine learning [...] Read more.
High-throughput single-cell RNA sequencing (scRNA-seq) has substantially advanced plant transcriptional landscapes. However, decoding cell-type-specific transcriptional regulation in non-model crops like tomato (Solanum lycopersicum) remains challenging. An integrated computational pipeline was applied using high-dimensional weighted gene co-expression (hdWGCNA) and ensemble machine learning to analyze tomato leaf single-cell transcriptomes. Unsupervised clustering identified 19 cell subpopulations mapped to five major cell-types: mesophyll cells (50.6%), guard cells (31.0%), trichomes (8.3%), vascular cells (7.5%), and lamina epidermis (2.6%). hdWGCNA revealed eight cell-type-specific modules, linking mesophyll cells to photosynthesis and guard cells to redox homeostasis. Machine learning classifiers prioritized candidate transcription factors (TFs), with XGBoost achieving the highest accuracy (0.85) to define cell identity. A consensus of 33 core TFs was identified, from which four candidate TFs (SlWRKY-78, SlWRKY-75, SlERF-57, and SlGLK-49) were selected for in silico knockout (KO) analysis. The simulations predicted that these knockouts might dysregulate core functional pathways, such as serine-type endopeptidase inhibitor activity and protein binding. Furthermore, CellOracle simulations suggested that the virtual deletion of the guard-cell-associated SlWRKY-78 and SlWRKY-75 could induce a directional trajectory shift from the terminally differentiated guard cells back to the less differentiated mesophyll territory. These findings provide a promising computational framework for deciphering cell-type-specific regulatory programs in horticultural crops. Full article
(This article belongs to the Special Issue Computational Approaches to Decoding Plant Molecular Networks)
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