AIMC Topic: Arabidopsis
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Model-to-crop conserved NUE Regulons enhance machine learning predictions of nitrogen use efficiency.
The Plant cell
May 9, 2025
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
May 1, 2025
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...
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...
HD-6mAPred: a hybrid deep learning approach for accurate prediction of N6-methyladenine sites in plant species.
PeerJ
Jan 1, 2025
BACKGROUND: N6-methyladenine (6mA) is an important DNA methylation modification that serves a crucial function in various biological activities. Accurate prediction of 6mA sites is essential for elucidating its biological function and underlying mech...
HNCGAT: a method for predicting plant metabolite-protein interaction using heterogeneous neighbor contrastive graph attention network.
Briefings in bioinformatics
Jul 25, 2024
The prediction of metabolite-protein interactions (MPIs) plays an important role in plant basic life functions. Compared with the traditional experimental methods and the high-throughput genomics methods using statistical correlation, applying hetero...
PTFSpot: deep co-learning on transcription factors and their binding regions attains impeccable universality in plants.
Briefings in bioinformatics
May 23, 2024
Unlike animals, variability in transcription factors (TFs) and their binding regions (TFBRs) across the plants species is a major problem that most of the existing TFBR finding software fail to tackle, rendering them hardly of any use. This limitatio...
miWords: transformer-based composite deep learning for highly accurate discovery of pre-miRNA regions across plant genomes.
Briefings in bioinformatics
Mar 19, 2023
Discovering pre-microRNAs (miRNAs) is the core of miRNA discovery. Using traditional sequence/structural features, many tools have been published to discover miRNAs. However, in practical applications like genomic annotations, their actual performanc...
3D Visualization of Microtubules in Epidermal Pavement Cells.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2023
The plant cytoskeleton is instrumental in cellular processes such as cell growth, differentiation, and immune response. Microtubules, in particular, play a crucial role in morphogenesis by governing the deposition of plant cell wall polysaccharides a...
iDHS-Deep: an integrated tool for predicting DNase I hypersensitive sites by deep neural network.
Briefings in bioinformatics
Sep 2, 2021
DNase I hypersensitive site (DHS) refers to the hypersensitive region of chromatin for the DNase I enzyme. It is an important part of the noncoding region and contains a variety of regulatory elements, such as promoter, enhancer, and transcription fa...
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...
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