AIMC Topic: Gene Expression Regulation, Plant

Clear Filters Showing 11 to 20 of 37 articles

DeepCBA: A deep learning framework for gene expression prediction in maize based on DNA sequences and chromatin interactions.

Plant communications Jun 10, 2024
Chromatin interactions create spatial proximity between distal regulatory elements and target genes in the genome, which has an important impact on gene expression, transcriptional regulation, and phenotypic traits. To date, several methods have been...
Deep Learning Promoter Regions, Genetic Gene Expression Regulation, Plant Chromatin Zea mays Base Sequence
View on PubMed DOI

Improving plant miRNA-target prediction with self-supervised k-mer embedding and spectral graph convolutional neural network.

PeerJ May 21, 2024
Deciphering the targets of microRNAs (miRNAs) in plants is crucial for comprehending their function and the variation in phenotype that they cause. As the highly cell-specific nature of miRNA regulation, recent computational approaches usually utiliz...
MicroRNAs Neural Networks, Computer Gene Expression Regulation, Plant RNA, Plant Computational Biology
View on PubMed DOI

An intelligent model for prediction of abiotic stress-responsive microRNAs in plants using statistical moments based features and ensemble approaches.

Methods (San Diego, Calif.) May 18, 2024
This study proposed an intelligent model for predicting abiotic stress-responsive microRNAs in plants. MicroRNAs (miRNAs) are short RNA molecules regulates the stress in genes. Experimental methods are costly and time-consuming, as compare to in-sili...
Gene Expression Regulation, Plant RNA, Plant Plants Algorithms Stress, Physiological Support Vector Machine Computational Biology MicroRNAs
View on PubMed DOI

Deep learning the cis-regulatory code for gene expression in selected model plants.

Nature communications Apr 25, 2024
Elucidating the relationship between non-coding regulatory element sequences and gene expression is crucial for understanding gene regulation and genetic variation. We explored this link with the training of interpretable deep learning models predict...
Zea mays Solanum lycopersicum Genetic Variation Deep Learning Regulatory Sequences, Nucleic Acid Genome, Plant Gene Expression Regulation, Plant Sorghum Arabidopsis Species Specificity
View on PubMed DOI

ASPTF: A computational tool to predict abiotic stress-responsive transcription factors in plants by employing machine learning algorithms.

Biochimica et biophysica acta. General subjects Mar 14, 2024
BACKGROUND: Abiotic stresses pose serious threat to the growth and yield of crop plants. Several studies suggest that in plants, transcription factors (TFs) are important regulators of gene expression, especially when it comes to coping with abiotic ...
Plants Plant Proteins Stress, Physiological Computational Biology Transcription Factors Gene Expression Regulation, Plant Machine Learning Algorithms
View on PubMed DOI

Deep learning-based quantification and transcriptomic profiling reveal a methyl jasmonate-mediated glandular trichome formation pathway in Cannabis sativa.

The Plant journal : for cell and molecular biology Feb 8, 2024
Cannabis glandular trichomes (GTs) are economically and biotechnologically important structures that have a remarkable morphology and capacity to produce, store, and secrete diverse classes of secondary metabolites. However, our understanding of the ...
Trichomes Oxylipins Cyclopentanes Cannabis Plant Growth Regulators Gene Expression Regulation, Plant Deep Learning Acetates Gene Expression Profiling Transcriptome
View on PubMed DOI

A deep learning approach for orphan gene identification in moso bamboo (Phyllostachys edulis) based on the CNN + Transformer model.

BMC bioinformatics May 5, 2022
BACKGROUND: Orphan gene play an important role in the environmental stresses of many species and their identification is a critical step to understand biological functions. Moso bamboo has high ecological, economic and cultural value. Studies have sh...
Deep Learning Gene Expression Regulation, Plant Amino Acids Poaceae
View on PubMed DOI

Screening and functional prediction of differentially expressed genes in walnut endocarp during hardening period based on deep neural network under agricultural internet of things.

PloS one Feb 24, 2022
The deep neural network is used to establish a neural network model to solve the problems of low accuracy and poor accuracy of traditional algorithms in screening differentially expressed genes and function prediction during the walnut endocarp harde...
Lignin Internet of Things Gene Expression Regulation, Plant Juglans Agriculture Seeds Fruit Neural Networks, Computer Algorithms
View on PubMed DOI

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...
Evolution, Molecular Machine Learning Genome, Plant Zea mays Genetic Variation Transcriptome Genotype Gene Expression Regulation, Plant Nitrogen Genomics Phenotype Models, Genetic Species Specificity Arabidopsis
View on PubMed DOI

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 ...
Quantitative Trait, Heritable Machine Learning Plant Leaves Genome-Wide Association Study Zea mays Gene Expression Regulation, Plant
View on PubMed DOI
Popular Topics
Recent Journals