AIMC Topic: Arabidopsis
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AtML: An Arabidopsis thaliana root cell identity recognition tool for medicinal ingredient accumulation.
Methods (San Diego, Calif.)
Sep 16, 2024
Arabidopsis thaliana synthesizes various medicinal compounds, and serves as a model plant for medicinal plant research. Single-cell transcriptomics technologies are essential for understanding the developmental trajectory of plant roots, facilitating...
Comparison of machine learning methods for genomic prediction of selected Arabidopsis thaliana traits.
PloS one
Aug 28, 2024
We present a comparison of machine learning methods for the prediction of four quantitative traits in Arabidopsis thaliana. High prediction accuracies were achieved on individuals grown under standardized laboratory conditions from the 1001 Arabidops...
A deep learning-based toolkit for 3D nuclei segmentation and quantitative analysis in cellular and tissue context.
Development (Cambridge, England)
Jul 18, 2024
We present a new set of computational tools that enable accurate and widely applicable 3D segmentation of nuclei in various 3D digital organs. We have developed an approach for ground truth generation and iterative training of 3D nuclear segmentation...
Machine learning assists prediction of genes responsible for plant specialized metabolite biosynthesis by integrating multi-omics data.
BMC genomics
Apr 29, 2024
BACKGROUND: Plant specialized (or secondary) metabolites (PSM), also known as phytochemicals, natural products, or plant constituents, play essential roles in interactions between plants and environment. Although many research efforts have focused on...
Deep learning the cis-regulatory code for gene expression in selected model plants.
Nature communications
Apr 25, 2024
Elucidating the relationship between non-coding regulatory element sequences and gene expression is crucial for understanding gene regulation and genetic variation. We explored this link with the training of interpretable deep learning models predict...
MAC-ErrorReads: machine learning-assisted classifier for filtering erroneous NGS reads.
BMC bioinformatics
Feb 7, 2024
BACKGROUND: The rapid advancement of next-generation sequencing (NGS) machines in terms of speed and affordability has led to the generation of a massive amount of biological data at the expense of data quality as errors become more prevalent. This i...
Prediction of Multiple Types of RNA Modifications via Biological Language Model.
IEEE/ACM transactions on computational biology and bioinformatics
Oct 9, 2023
It has been demonstrated that RNA modifications play essential roles in multiple biological processes. Accurate identification of RNA modifications in the transcriptome is critical for providing insights into the biological functions and mechanisms. ...
Predicting Plant miRNA-lncRNA Interactions via a Deep Learning Method.
IEEE transactions on nanobioscience
Oct 3, 2023
In recent years, due to the contribution to elucidating the functional mechanisms of miRNAs and lncRNAs, the research on miRNA-lncRNA interaction prediction has increased exponentially. However, the prediction research is challenging in bioinformatic...
Deep learning-assisted prediction of protein-protein interactions in Arabidopsis thaliana.
The Plant journal : for cell and molecular biology
Mar 29, 2023
Currently, the experimentally identified interactome of Arabidopsis (Arabidopsis thaliana) is still far from complete, suggesting that computational prediction methods can complement experimental techniques. Motivated by the prosperity and success of...
I-DNAN6mA: Accurate Identification of DNA N-Methyladenine Sites Using the Base-Pairing Map and Deep Learning.
Journal of chemical information and modeling
Feb 1, 2023
The recent discovery of numerous DNA N-methyladenine (6mA) sites has transformed our perception about the roles of 6mA in living organisms. However, our ability to understand them is hampered by our inability to identify 6mA sites rapidly and cost-ef...
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