AIMC Topic: Bacteria

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Improving the odds: Artificial intelligence and the great plate count anomaly.

Microbial biotechnology
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
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
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)
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)
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
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
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
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...

Large-scale genomic survey with deep learning-based method reveals strain-level phage specificity determinants.

GigaScience
BACKGROUND: Phage therapy, reemerging as a promising approach to counter antimicrobial-resistant infections, relies on a comprehensive understanding of the specificity of individual phages. Yet the significant diversity within phage populations prese...

Coracle-a machine learning framework to identify bacteria associated with continuous variables.

Bioinformatics (Oxford, England)
SUMMARY: We present Coracle, an artificial intelligence (AI) framework that can identify associations between bacterial communities and continuous variables. Coracle uses an ensemble approach of prominent feature selection methods and machine learnin...