AIMC Topic: Arabidopsis Proteins

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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...
Software Deep Learning Proteome Internet Molecular Sequence Annotation Arabidopsis Proteins Arabidopsis
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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...
Machine Learning Gene Expression Profiling Models, Biological Circadian Rhythm Phytochrome Circadian Clocks Gene Expression Regulation, Plant Arabidopsis Proteins Arabidopsis Regulatory Sequences, Nucleic Acid Ecotype Apoproteins Phytochrome A
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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...
Machine Learning Gene Expression Profiling Deep Learning Single-Cell Analysis RNA-Seq Gene Regulatory Networks Transcription Factors Datasets as Topic Stress, Physiological Promoter Regions, Genetic Gene Expression Regulation, Plant Genes, Plant Arabidopsis Proteins Arabidopsis Genes, Regulator
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