AIMC Topic: Arabidopsis Proteins

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DeepTetrad: high-throughput image analysis of meiotic tetrads by deep learning in Arabidopsis thaliana.

The Plant journal : for cell and molecular biology Oct 22, 2019
Meiotic crossovers facilitate chromosome segregation and create new combinations of alleles in gametes. Crossover frequency varies along chromosomes and crossover interference limits the coincidence of closely spaced crossovers. Crossovers can be mea...
Crossing Over, Genetic Homologous Recombination Chromosome Segregation Chromosomes, Plant Deep Learning Alleles Transgenes Arabidopsis Pollen Meiosis Arabidopsis Proteins
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Enabling full-length evolutionary profiles based deep convolutional neural network for predicting DNA-binding proteins from sequence.

Proteins Jul 8, 2019
Sequence based DNA-binding protein (DBP) prediction is a widely studied biological problem. Sliding windows on position specific substitution matrices (PSSMs) rows predict DNA-binding residues well on known DBPs but the same models cannot be applied ...
Humans Neural Networks, Computer Animals Mice DNA Amino Acids Binding Sites DNA-Binding Proteins Arabidopsis Proteins Models, Biological Arabidopsis Protein Conformation
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A machine learning based framework to identify and classify long terminal repeat retrotransposons.

PLoS computational biology Apr 23, 2018
Transposable elements (TEs) are repetitive nucleotide sequences that make up a large portion of eukaryotic genomes. They can move and duplicate within a genome, increasing genome size and contributing to genetic diversity within and across species. A...
Genome, Insect Retroelements Terminal Repeat Sequences DNA, Plant Arabidopsis Proteins Arabidopsis Software Animals Computational Biology Machine Learning Drosophila melanogaster Decision Trees Genome, Plant Evolution, Molecular Conserved Sequence Drosophila Proteins
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Measuring semantic similarities by combining gene ontology annotations and gene co-function networks.

BMC bioinformatics Feb 14, 2015
BACKGROUND: Gene Ontology (GO) has been used widely to study functional relationships between genes. The current semantic similarity measures rely only on GO annotations and GO structure. This limits the power of GO-based similarity because of the li...
Saccharomyces cerevisiae Proteins Multigene Family Gene Regulatory Networks Arabidopsis Proteins Cytochrome P-450 Enzyme System Humans Computational Biology Vocabulary, Controlled Semantics Software Metabolic Networks and Pathways Molecular Sequence Annotation Algorithms Gene Ontology
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Machine-learning meta-analysis reveals ethylene as a central component of the molecular core in abiotic stress responses in Arabidopsis.

Nature communications May 22, 2025
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....
Machine Learning Gene Expression Profiling Transcriptome Gene Expression Regulation, Plant Plant Roots Arabidopsis Proteins Arabidopsis Stress, Physiological Ethylenes Plant Shoots
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AtSubP-2.0: An integrated web server for the annotation of Arabidopsis proteome subcellular localization using deep learning.

The plant genome Mar 1, 2025
The organization of subcellular components in a cell is critical for its function and studying cellular processes, protein-protein interactions, identifying potential drug targets, network analysis, and other systems biology mechanisms. Determining p...
Molecular Sequence Annotation Arabidopsis Proteins Arabidopsis Software Deep Learning Proteome Internet
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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 Aug 10, 2021
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...
Models, Biological Circadian Rhythm Phytochrome Machine Learning Gene Expression Profiling Apoproteins Phytochrome A Arabidopsis Circadian Clocks Regulatory Sequences, Nucleic Acid Gene Expression Regulation, Plant Ecotype Arabidopsis Proteins
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Prediction of condition-specific regulatory genes using machine learning.

Nucleic acids research Jun 19, 2020
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...
Genes, Plant Arabidopsis Proteins Arabidopsis Stress, Physiological Promoter Regions, Genetic Gene Expression Regulation, Plant Genes, Regulator Deep Learning Single-Cell Analysis RNA-Seq Gene Regulatory Networks Transcription Factors Datasets as Topic Machine Learning Gene Expression Profiling
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