AIMC Topic: Gene Expression Regulation, Plant

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Transcripts and genomic intervals associated with variation in metabolite abundance in maize leaves under field conditions.

BMC genomics May 1, 2025
Plants exhibit extensive environment-dependent intraspecific metabolic variation, which likely plays a role in determining variation in whole plant phenotypes. However, much of the work seeking to use natural variation to link genes and transcript's ...
Genome-Wide Association Study Gene Expression Regulation, Plant Phenotype Plant Leaves Genomics Quantitative Trait Loci Transcriptome Zea mays Metabolome Genome, Plant
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Deep Neural Network-Mining of Rice Drought-Responsive TF-TAG Modules by a Combinatorial Analysis of ATAC-Seq and RNA-Seq.

Plant, cell & environment Mar 31, 2025
Drought is a critical risk factor that impacts rice growth and yields. Previous studies have focused on the regulatory roles of individual transcription factors in response to drought stress. However, there is limited understanding of multi-factor st...
Gene Regulatory Networks Transcription Factors Oryza Neural Networks, Computer RNA-Seq Gene Expression Regulation, Plant Droughts Chromatin Immunoprecipitation Sequencing Data Mining Plant Proteins Stress, Physiological
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Cell Wall-Based Machine Learning Models to Predict Plant Growth Using Onion Epidermis.

International journal of molecular sciences Mar 24, 2025
The plant cell wall (CW) is a physical barrier that plays a dual role in plant physiology, providing structural support for growth and development. Understanding the dynamics of CW growth is crucial for optimizing crop yields. In this study, we emplo...
Spectroscopy, Fourier Transform Infrared Cellulose Cell Wall Machine Learning Support Vector Machine Plant Epidermis Onions Gene Expression Regulation, Plant
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Feedback regulation of mA modification creates local auxin maxima essential for rice microsporogenesis.

Developmental cell Jan 13, 2025
N-methyladenosine (mA) RNA modification and its effectors control various plant developmental processes, yet whether and how these effectors are transcriptionally controlled to confer functional specificity so far remain elusive. Herein, we show that...
Gametogenesis, Plant Plant Proteins Indoleacetic Acids Adenosine Meiosis Oryza Gene Expression Regulation, Plant Flowers Feedback, Physiological
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Phenotype prediction in plants is improved by integrating large-scale transcriptomic datasets.

NAR genomics and bioinformatics Dec 27, 2024
Research on the dynamic expression of genes in plants is important for understanding different biological processes. We used the large amounts of transcriptomic data from various plant sample sources that are publicly available to investigate whether...
Databases, Genetic Zea mays Oryza Phenotype Gene Expression Profiling Transcriptome Gene Expression Regulation, Plant Machine Learning
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Machine learning-based identification of general transcriptional predictors for plant disease.

The New phytologist Nov 21, 2024
This study investigated the generalizability of Arabidopsis thaliana immune responses across diverse pathogens, including Botrytis cinerea, Sclerotinia sclerotiorum, and Pseudomonas syringae, using a data-driven, machine learning approach. Machine le...
Gene Expression Regulation, Plant Arabidopsis Botrytis Pseudomonas syringae Plant Immunity Ascomycota Transcription, Genetic Disease Resistance Transcriptome Plant Diseases Machine Learning Reproducibility of Results
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Machine learning-aided microRNA discovery for olive oil quality.

PloS one Oct 11, 2024
MicroRNAs (miRNAs) are key regulators of gene expression in plants, influencing various biological processes such as oil quality and seed development. Although, our knowledge about miRNAs in olive (Olea europaea L.) is progressing, with several miRNA...
Olive Oil Gene Expression Regulation, Plant Machine Learning Support Vector Machine RNA, Plant MicroRNAs Olea
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AtML: An Arabidopsis thaliana root cell identity recognition tool for medicinal ingredient accumulation.

Methods (San Diego, Calif.) Sep 16, 2024
Arabidopsis thaliana synthesizes various medicinal compounds, and serves as a model plant for medicinal plant research. Single-cell transcriptomics technologies are essential for understanding the developmental trajectory of plant roots, facilitating...
Plant Roots Gene Expression Profiling Arabidopsis Plants, Medicinal Machine Learning Transcriptome Gene Expression Regulation, Plant Single-Cell Analysis
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Deep learning can predict subgenome dominance in ancient but not in neo/synthetic polyploidized genomes.

The Plant journal : for cell and molecular biology Aug 12, 2024
Deep learning offers new approaches to investigate the mechanisms underlying complex biological phenomena, such as subgenome dominance. Subgenome dominance refers to the dominant expression and/or biased fractionation of genes in one subgenome of all...
Neural Networks, Computer Deep Learning DNA Methylation Evolution, Molecular Promoter Regions, Genetic Genome, Plant Gene Expression Regulation, Plant Polyploidy
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Utilizing machine learning and bioinformatics analysis to identify drought-responsive genes affecting yield in foxtail millet.

International journal of biological macromolecules Jul 29, 2024
Drought stress is a major constraint on crop development, potentially causing huge yield losses and threatening global food security. Improving Crop's stress tolerance is usually associated with a yield penalty. One way to balance yield and stress to...
Gene Expression Regulation, Plant Droughts Genes, Plant Computational Biology Plant Proteins Setaria Plant Stress, Physiological Haplotypes Machine Learning Gene Expression Profiling Transcriptome
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