AIMC Topic: Gene Expression Regulation, Plant
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Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships.
Nature communications
Sep 24, 2021
Inferring phenotypic outcomes from genomic features is both a promise and challenge for systems biology. Using gene expression data to predict phenotypic outcomes, and functionally validating the genes with predictive powers are two challenges we add...
Machine Learning Enables High-Throughput Phenotyping for Analyses of the Genetic Architecture of Bulliform Cell Patterning in Maize.
G3 (Bethesda, Md.)
Dec 3, 2019
Bulliform cells comprise specialized cell types that develop on the adaxial (upper) surface of grass leaves, and are patterned to form linear rows along the proximodistal axis of the adult leaf blade. Bulliform cell patterning affects leaf angle and ...
Identification of the expressome by machine learning on omics data.
Proceedings of the National Academy of Sciences of the United States of America
Aug 16, 2019
Accurate annotation of plant genomes remains complex due to the presence of many pseudogenes arising from whole-genome duplication-generated redundancy or the capture and movement of gene fragments by transposable elements. Machine learning on genome...
Differential gene expression and gene ontologies associated with increasing water-stress in leaf and root transcriptomes of perennial ryegrass (Lolium perenne).
PloS one
Jul 30, 2019
Perennial ryegrass (Lolium perenne) is a forage and amenity grass species widely cultivated in temperate regions worldwide. As such, perennial ryegrass populations are exposed to a range of environmental conditions and stresses on a seasonal basis an...
De novo assembly of Agave sisalana transcriptome in response to drought stress provides insight into the tolerance mechanisms.
Scientific reports
Jan 23, 2019
Agave, monocotyledonous succulent plants, is endemic to arid regions of North America, exhibiting exceptional tolerance to their xeric environments. They employ various strategies to overcome environmental constraints, such as crassulacean acid metab...
Comparative Analysis of the Cytology and Transcriptomes of the Cytoplasmic Male Sterility Line H276A and Its Maintainer Line H276B of Cotton (Gossypium barbadense L.).
International journal of molecular sciences
Oct 25, 2017
In this study, the tetrad stage of microspore development in a new cotton ( L.) cytoplasmic male sterility (CMS) line, H276A, was identified using paraffin sections at the abortion stage. To explore the molecular mechanism underlying CMS in cotton, a...
Interspecies predictions of growth traits from quantitative transcriptome data acquired during fruit development.
Journal of experimental botany
Aug 21, 2025
Linking genotype and phenotype is a fundamental challenge in biology. In this respect, machine learning is playing a pivotal role in systems biology. As central phenotypic traits, fruit development and relative growth rate (RGR) result from interacti...
Machine learning based prediction by PlantCdMiner and experimental validation of cadmium-responsive genes in plants.
Journal of hazardous materials
Aug 15, 2025
Plants have evolved diverse adaptive mechanisms to sense and respond to environmental stimuli such as cadmium stress. The regulation of gene expression plays a critical role in plant responses to abiotic stress. However, homologous genes from differe...
Structure and function of Vitis vinifera Arabidopsis Response Regulator 1 (VvARR1) protein provide insights into the regulatory mechanism of grape fruit shape through gibberellin-cytokinin crosstalk.
International journal of biological macromolecules
Aug 1, 2025
Grape fruit shape is a crucial agricultural trait that significantly impacts the commercial value of grapes. In this study, we developed a machine learning system to classify grape fruit shapes, offering an objective phenotypic assessment method. Mul...
Machine-learning meta-analysis reveals ethylene as a central component of the molecular core in abiotic stress responses in Arabidopsis.
Nature communications
May 22, 2025
Understanding how plants adapt their physiology to overcome severe and often multifactorial stress conditions in nature is vital in light of the climate crisis. This remains a challenge given the complex nature of the underlying molecular mechanisms....
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