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Gene Expression Regulation, Plant

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Differential gene expression and gene ontologies associated with increasing water-stress in leaf and root transcriptomes of perennial ryegrass (Lolium perenne).

PloS one
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
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...

Identification of the expressome by machine learning on omics data.

Proceedings of the National Academy of Sciences of the United States of America
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...

High-throughput phenotyping with deep learning gives insight into the genetic architecture of flowering time in wheat.

GigaScience
BACKGROUND: Measurement of plant traits with precision and speed on large populations has emerged as a critical bottleneck in connecting genotype to phenotype in genetics and breeding. This bottleneck limits advancements in understanding plant genome...

Machine Learning Enables High-Throughput Phenotyping for Analyses of the Genetic Architecture of Bulliform Cell Patterning in Maize.

G3 (Bethesda, Md.)
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 ...

An Overview on Predicting Protein Subchloroplast Localization by using Machine Learning Methods.

Current protein & peptide science
The chloroplast is a type of subcellular organelle of green plants and eukaryotic algae, which plays an important role in the photosynthesis process. Since the function of a protein correlates with its location, knowing its subchloroplast localizatio...

Prediction of condition-specific regulatory genes using machine learning.

Nucleic acids research
Recent advances in genomic technologies have generated data on large-scale protein-DNA interactions and open chromatin regions for many eukaryotic species. How to identify condition-specific functions of transcription factors using these data has bec...

Prediction of Rice Transcription Start Sites Using TransPrise: A Novel Machine Learning Approach.

Methods in molecular biology (Clifton, N.J.)
As the interest in genetic resequencing increases, so does the need for effective mathematical, computational, and statistical approaches. One of the difficult problems in genome annotation is determination of precise positions of transcription start...

Interpreting machine learning models to investigate circadian regulation and facilitate exploration of clock function.

Proceedings of the National Academy of Sciences of the United States of America
The circadian clock is an important adaptation to life on Earth. Here, we use machine learning to predict complex, temporal, and circadian gene expression patterns in Most significantly, we classify circadian genes using DNA sequence features genera...

Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships.

Nature communications
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...