AIMC Topic: Bacteria
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DeepPBI-KG: a deep learning method for the prediction of phage-bacteria interactions based on key genes.
Briefings in bioinformatics
Sep 23, 2024
Phages, the natural predators of bacteria, were discovered more than 100 years ago. However, increasing antimicrobial resistance rates have revitalized phage research. Methods that are more time-consuming and efficient than wet-laboratory experiments...
Identification of putative coral pathogens in endangered Caribbean staghorn coral using machine learning.
Environmental microbiology
Sep 1, 2024
Coral diseases contribute to the rapid decline in coral reefs worldwide, and yet coral bacterial pathogens have proved difficult to identify because 16S rRNA gene surveys typically identify tens to hundreds of disease-associate bacteria as putative p...
Improving the odds: Artificial intelligence and the great plate count anomaly.
Microbial biotechnology
Sep 1, 2024
Next-generation DNA sequencing has shown that the great plate count anomaly, that is, the difference between bacteria present in the environment and those that can be obtained in culture from that environment, is even greater and more persisting than...
Predicting the role of the human gut microbiome in type 1 diabetes using machine-learning methods.
Briefings in functional genomics
Jul 19, 2024
Gut microbes is a crucial factor in the pathogenesis of type 1 diabetes (T1D). However, it is still unclear which gut microbiota are the key factors affecting T1D and their influence on the development and progression of the disease. To fill these kn...
Spatio-temporal changes of small protist and free-living bacterial communities in a temperate dimictic lake: insights from metabarcoding and machine learning.
FEMS microbiology ecology
Jul 12, 2024
Microbial communities, which include prokaryotes and protists, play an important role in aquatic ecosystems and influence ecological processes. To understand these communities, metabarcoding provides a powerful tool to assess their taxonomic composit...
A deep learning method to predict bacterial ADP-ribosyltransferase toxins.
Bioinformatics (Oxford, England)
Jul 1, 2024
MOTIVATION: ADP-ribosylation is a critical modification involved in regulating diverse cellular processes, including chromatin structure regulation, RNA transcription, and cell death. Bacterial ADP-ribosyltransferase toxins (bARTTs) serve as potent v...
MotGen: a closed-loop bacterial motility control framework using generative adversarial networks.
Bioinformatics (Oxford, England)
Mar 29, 2024
MOTIVATION: Many organisms' survival and behavior hinge on their responses to environmental signals. While research on bacteria-directed therapeutic agents has increased, systematic exploration of real-time modulation of bacterial motility remains li...
DSNetax: a deep learning species annotation method based on a deep-shallow parallel framework.
Briefings in bioinformatics
Mar 27, 2024
Microbial community analysis is an important field to study the composition and function of microbial communities. Microbial species annotation is crucial to revealing microorganisms' complex ecological functions in environmental, ecological and host...
A microbial knowledge graph-based deep learning model for predicting candidate microbes for target hosts.
Briefings in bioinformatics
Mar 27, 2024
Predicting interactions between microbes and hosts plays critical roles in microbiome population genetics and microbial ecology and evolution. How to systematically characterize the sophisticated mechanisms and signal interplay between microbes and h...
Deepdefense: annotation of immune systems in prokaryotes using deep learning.
GigaScience
Jan 2, 2024
BACKGROUND: Due to a constant evolutionary arms race, archaea and bacteria have evolved an abundance and diversity of immune responses to protect themselves against phages. Since the discovery and application of CRISPR-Cas adaptive immune systems, nu...
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