AIMC Topic: Arabidopsis

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NBSPred: a support vector machine-based high-throughput pipeline for plant resistance protein NBSLRR prediction.

Bioinformatics (Oxford, England)
UNLABELLED: The nucleotide binding site leucine-rich repeats (NBSLRRs) belong to one of the largest known families of disease resistance genes that encode resistance proteins (R-protein) against the pathogens of plants. Various defence mechanisms hav...

DNA-binding protein prediction using plant specific support vector machines: validation and application of a new genome annotation tool.

Nucleic acids research
There are currently 151 plants with draft genomes available but levels of functional annotation for putative protein products are low. Therefore, accurate computational predictions are essential to annotate genomes in the first instance, and to provi...

Use of Semisupervised Clustering and Feature-Selection Techniques for Identification of Co-expressed Genes.

IEEE journal of biomedical and health informatics
Studying the patterns hidden in gene-expression data helps to understand the functionality of genes. In general, clustering techniques are widely used for the identification of natural partitionings from the gene expression data. In order to put cons...

Alpha-plane based automatic general type-2 fuzzy clustering based on simulated annealing meta-heuristic algorithm for analyzing gene expression data.

Computers in biology and medicine
This paper considers microarray gene expression data clustering using a novel two stage meta-heuristic algorithm based on the concept of α-planes in general type-2 fuzzy sets. The main aim of this research is to present a powerful data clustering app...

Coincidence of the threshold temperature of seasonal switching for diel transcriptomic oscillations and growth.

Plant & cell physiology
Predicting plant responses to global warming is essential for ecosystem management and crop yields. As many genes are controlled by the circadian clock, understanding the effects of temperature on transcriptomic rhythmicity under natural conditions i...

Machine learning based prediction by PlantCdMiner and experimental validation of cadmium-responsive genes in plants.

Journal of hazardous materials
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...

Methylomes Reveal Recent Evolutionary Changes in Populations of Two Plant Species.

Genome biology and evolution
Plant DNA methylation changes occur hundreds to thousands of times faster than DNA mutations and can be transmitted transgenerationally, making them useful for studying population-scale patterns in clonal or selfing species. However, a state-of-the-a...

Machine-learning meta-analysis reveals ethylene as a central component of the molecular core in abiotic stress responses in Arabidopsis.

Nature communications
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....

Model-to-crop conserved NUE Regulons enhance machine learning predictions of nitrogen use efficiency.

The Plant cell
Systems biology aims to uncover gene regulatory networks (GRNs) for agricultural traits, but validating them in crops is challenging. We addressed this challenge by learning and validating model-to-crop transcription factor (TF) regulons governing ni...

Machine learning-augmented m6A-Seq analysis without a reference genome.

Briefings in bioinformatics
Methylated RNA m6A immunoprecipitation sequencing (m6A-Seq) is a powerful technique for investigating transcriptome-wide m6A modification. However, most of the existing m6A-Seq protocols rely on reference genomes, limiting their use in species lackin...