AIMC Topic: Arabidopsis

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PrUb-EL: A hybrid framework based on deep learning for identifying ubiquitination sites in Arabidopsis thaliana using ensemble learning strategy.

Analytical biochemistry Oct 4, 2022
Identification of ubiquitination sites is central to many biological experiments. Ubiquitination is a kind of post-translational protein modification (PTM). It is a key mechanism for increasing protein diversity and plays a vital role in regulating c...
Dipeptides Ubiquitination Arabidopsis Ubiquitinated Proteins Deep Learning Amino Acids
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An automatic method to quantify trichomes in Arabidopsis thaliana.

Plant science : an international journal of experimental plant biology Jul 19, 2022
Trichomes are unicellular or multicellular hair-like appendages developed on the aerial plant epidermis of most plant species that act as a protective barrier against natural hazards. For this reason, evaluating the density of trichomes is a valuable...
Machine Learning Reproducibility of Results Arabidopsis Proteins Arabidopsis Trichomes Plant Epidermis
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A novel deep learning-based 3D cell segmentation framework for future image-based disease detection.

Scientific reports Jan 10, 2022
Cell segmentation plays a crucial role in understanding, diagnosing, and treating diseases. Despite the recent success of deep learning-based cell segmentation methods, it remains challenging to accurately segment densely packed cells in 3D cell memb...
Image Interpretation, Computer-Assisted Reproducibility of Results Cell Membrane Imaging, Three-Dimensional Animals Embryo, Nonmammalian Deep Learning Predictive Value of Tests Arabidopsis Zebrafish Microscopy
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A novel strategy to uncover specific GO terms/phosphorylation pathways in phosphoproteomic data in Arabidopsis thaliana.

BMC plant biology Dec 14, 2021
BACKGROUND: Proteins are the workforce of the cell and their phosphorylation status tailors specific responses efficiently. One of the main challenges of phosphoproteomic approaches is to deconvolute biological processes that specifically respond to ...
Proteome Databases, Protein Phosphorylation Seedlings RNA, Messenger RNA, Plant Genes, Plant RNA Splicing Arabidopsis Proteins Phosphoproteins Arabidopsis Microtubules Gene Ontology
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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...
Gene Expression Regulation, Plant Arabidopsis Genotype Models, Genetic Evolution, Molecular Machine Learning Zea mays Phenotype Transcriptome Nitrogen Genomics Genetic Variation Species Specificity Genome, Plant
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A novel lncRNA-protein interaction prediction method based on deep forest with cascade forest structure.

Scientific reports Sep 23, 2021
Long noncoding RNAs (lncRNAs) regulate many biological processes by interacting with corresponding RNA-binding proteins. The identification of lncRNA-protein Interactions (LPIs) is significantly important to well characterize the biological functions...
Machine Learning Computational Biology RNA-Binding Proteins RNA, Long Noncoding Humans Zea mays Datasets as Topic Arabidopsis
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Robotic Assay for Drought (RoAD): an automated phenotyping system for brassinosteroid and drought responses.

The Plant journal : for cell and molecular biology Aug 10, 2021
Brassinosteroids (BRs) are a group of plant steroid hormones involved in regulating growth, development, and stress responses. Many components of the BR pathway have previously been identified and characterized. However, BR phenotyping experiments ar...
Arabidopsis Proteins Arabidopsis Brassinosteroids Image Processing, Computer-Assisted Machine Learning Equipment Design Robotics Phenotype Protein Kinases Seedlings Soil Triazoles Zea mays Droughts
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Machine-learning predicts genomic determinants of meiosis-driven structural variation in a eukaryotic pathogen.

Nature communications Jun 10, 2021
Species harbor extensive structural variation underpinning recent adaptive evolution. However, the causality between genomic features and the induction of new rearrangements is poorly established. Here, we analyze a global set of telomere-to-telomere...
Ascomycota Pedigree Meiosis Eukaryota Genome Machine Learning INDEL Mutation Computer Simulation Chromosomes Phylogeny Arabidopsis Models, Genetic Evolution, Molecular Genome-Wide Association Study Sequence Inversion Genetic Variation Genomics Polymorphism, Genetic Retroelements Polymorphism, Single Nucleotide Crossing Over, Genetic Genes, Duplicate
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Deep6mA: A deep learning framework for exploring similar patterns in DNA N6-methyladenine sites across different species.

PLoS computational biology Feb 18, 2021
N6-methyladenine (6mA) is an important DNA modification form associated with a wide range of biological processes. Identifying accurately 6mA sites on a genomic scale is crucial for under-standing of 6mA's biological functions. However, the existing ...
DNA Methylation Neural Networks, Computer Species Specificity Rosa DNA Deep Learning Fragaria Databases, Nucleic Acid Oryza Computational Biology Binding Sites DNA, Plant Base Sequence Adenine Arabidopsis
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Computational identification of ubiquitination sites in Arabidopsis thaliana using convolutional neural networks.

Plant molecular biology Feb 1, 2021
We developed two CNNs for predicting ubiquitination sites in Arabidopsis thaliana, demonstrated their competitive performance, analyzed amino acid physicochemical properties and the CNN structures, and predicted ubiquitination sites in Arabidopsis. A...
Humans Arabidopsis Protein Processing, Post-Translational Animals Neural Networks, Computer Mice Amino Acids Software Computational Biology Yeasts Proteome Ubiquitination
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