AIMC Topic: Gene Expression Regulation, Plant

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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 Genome, Plant Gene Expression Regulation, Plant Polyploidy Evolution, Molecular Promoter Regions, Genetic
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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...
Droughts Genes, Plant Gene Expression Profiling Stress, Physiological Transcriptome Setaria Plant Gene Expression Regulation, Plant Plant Proteins Computational Biology Haplotypes Machine Learning
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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...
Base Sequence Promoter Regions, Genetic Gene Expression Regulation, Plant Deep Learning Zea mays Chromatin
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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...
Gene Expression Regulation, Plant Neural Networks, Computer RNA, Plant Computational Biology MicroRNAs
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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...
Stress, Physiological Gene Expression Regulation, Plant RNA, Plant MicroRNAs Plants Computational Biology Support Vector Machine Algorithms
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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...
Arabidopsis Regulatory Sequences, Nucleic Acid Species Specificity Genome, Plant Genetic Variation Gene Expression Regulation, Plant Sorghum Zea mays Solanum lycopersicum Deep Learning
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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 ...
Stress, Physiological Transcription Factors Machine Learning Gene Expression Regulation, Plant Algorithms Plants Plant Proteins Computational Biology
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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 ...
Gene Expression Regulation, Plant Trichomes Cyclopentanes Oxylipins Acetates Cannabis Plant Growth Regulators Transcriptome Deep Learning Gene Expression Profiling
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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...
Poaceae Gene Expression Regulation, Plant Deep Learning Amino Acids
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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...
Fruit Lignin Agriculture Juglans Neural Networks, Computer Internet of Things Gene Expression Regulation, Plant Seeds Algorithms
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