AIMC Topic: Arabidopsis
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A generalized platform for artificial intelligence-powered autonomous enzyme engineering.
Nature communications
Jul 1, 2025
Proteins are the molecular machines of life with numerous applications in energy, health, and sustainability. However, engineering proteins with desired functions for practical applications remains slow, expensive, and specialist-dependent. Here we r...
An NLP-based method to mine gene and function relationships from published articles.
Scientific reports
Mar 3, 2025
Understanding the intricacies of genes function within biological systems is paramount for scientific advancement and medical progress. Owing to the evolving landscape of this research and the complexity of biological processes, however, this task pr...
Deep Learning Enhances Precision of Citrullination Identification in Human and Plant Tissue Proteomes.
Molecular & cellular proteomics : MCP
Feb 5, 2025
Citrullination is a critical yet understudied post-translational modification (PTM) implicated in various biological processes. Exploring its role in health and disease requires a comprehensive understanding of the prevalence of this PTM at a proteom...
Lessons from Deep Learning Structural Prediction of Multistate Multidomain Proteins-The Case Study of Coiled-Coil NOD-like Receptors.
International journal of molecular sciences
Jan 9, 2025
We test here the prediction capabilities of the new generation of deep learning predictors in the more challenging situation of multistate multidomain proteins by using as a case study a coiled-coil family of Nucleotide-binding Oligomerization Domain...
Deep learning-based cytoskeleton segmentation for accurate high-throughput measurement of cytoskeleton density.
Protoplasma
Dec 18, 2024
Microscopic analyses of cytoskeleton organization are crucial for understanding various cellular activities, including cell proliferation and environmental responses in plants. Traditionally, assessments of cytoskeleton dynamics have been qualitative...
Ense-i6mA: Identification of DNA N-Methyladenine Sites Using XGB-RFE Feature Selection and Ensemble Machine Learning.
IEEE/ACM transactions on computational biology and bioinformatics
Dec 10, 2024
DNA N-methyladenine (6mA) is an important epigenetic modification that plays a vital role in various cellular processes. Accurate identification of the 6mA sites is fundamental to elucidate the biological functions and mechanisms of modification. How...
Machine learning-based identification of general transcriptional predictors for plant disease.
The New phytologist
Nov 21, 2024
This study investigated the generalizability of Arabidopsis thaliana immune responses across diverse pathogens, including Botrytis cinerea, Sclerotinia sclerotiorum, and Pseudomonas syringae, using a data-driven, machine learning approach. Machine le...
Enhancing Arabidopsis thaliana ubiquitination site prediction through knowledge distillation and natural language processing.
Methods (San Diego, Calif.)
Oct 22, 2024
Protein ubiquitination is a critical post-translational modification (PTM) involved in diverse biological processes and plays a pivotal role in regulating physiological mechanisms and disease states. Despite various efforts to develop ubiquitination ...
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...
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