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Bacteria

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Bacterial Autonomous Intelligent Microrobots for Biomedical Applications.

Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnology
Micro/nanorobots are being increasingly utilized as new diagnostic and therapeutic platforms in the biomedical field, enabling remote navigation to hard-to-reach tissues and the execution of various medical procedures. Although significant progress h...

Investigating the Impact of Antibiotics on Environmental Microbiota Through Machine Learning Models.

IET systems biology
Antibiotic pollution in the environment can significantly impact soil microorganisms, such as altering the soil microbial community or emerging antibiotic-resistant bacteria. We propose three machine learning (ML) methods to investigate antibiotics' ...

Using GWAS and Machine Learning to Identify and Predict Genetic Variants Associated with Foodborne Bacteria Phenotypic Traits.

Methods in molecular biology (Clifton, N.J.)
One of the main challenges in food microbiology is to prevent the risk of outbreaks by avoiding the distribution of food contaminated by bacteria. This requires constant monitoring of the circulating strains throughout the food production chain. Bact...

Integrating MALDI-TOF Mass Spectrometry with Machine Learning Techniques for Rapid Antimicrobial Resistance Screening of Foodborne Bacterial Pathogens.

Methods in molecular biology (Clifton, N.J.)
Although MALDI-TOF mass spectrometry (MS) is considered as the gold standard for rapid and cost-effective identification of microorganisms in routine laboratory practices, its capability for antimicrobial resistance (AMR) detection has received limit...

Deep learning revealed the distribution and evolution patterns for invertible promoters across bacterial lineages.

Nucleic acids research
Invertible promoters (invertons) are crucial regulatory elements in bacteria, facilitating gene expression changes under stress. Despite their importance, their prevalence and the range of regulated gene functions are largely unknown. We introduced D...

Predicting bacterial transcription factor binding sites through machine learning and structural characterization based on DNA duplex stability.

Briefings in bioinformatics
Transcriptional factors (TFs) in bacteria play a crucial role in gene regulation by binding to specific DNA sequences, thereby assisting in the activation or repression of genes. Despite their central role, deciphering shape recognition of bacterial ...

DeepPBI-KG: a deep learning method for the prediction of phage-bacteria interactions based on key genes.

Briefings in bioinformatics
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
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
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...