AIMC Topic: Bacteria
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Deep-Learning-Based Approaches for Rational Design of Stapled Peptides With High Antimicrobial Activity and Stability.
Microbial biotechnology
Mar 1, 2025
Antimicrobial peptides (AMPs) face stability and toxicity challenges in clinical use. Stapled modification enhances their stability and effectiveness, but its application in peptide design is rarely reported. This study built ten prediction models fo...
Predicting the bacterial host range of plasmid genomes using the language model-based one-class support vector machine algorithm.
Microbial genomics
Feb 1, 2025
The prediction of the plasmid host range is crucial for investigating the dissemination of plasmids and the transfer of resistance and virulence genes mediated by plasmids. Several machine learning-based tools have been developed to predict plasmid h...
Reviewing on AI-Designed Antibiotic Targeting Drug-Resistant Superbugs by Emphasizing Mechanisms of Action.
Drug development research
Feb 1, 2025
The emergence of drug-resistant bacteria, often referred to as "superbugs," poses a profound and escalating challenge to global health systems, surpassing the capabilities of traditional antibiotic discovery methods. As resistance mechanisms evolve r...
A machine-learning approach for predicting butyrate production by microbial consortia using metabolic network information.
PeerJ
Jan 1, 2025
Understanding the behavior of microbial consortia is crucial for predicting metabolite production by microorganisms. Genome-scale network reconstructions enable the computation of metabolic interactions and specific associations within microbial cons...
Bacterial Autonomous Intelligent Microrobots for Biomedical Applications.
Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnology
Jan 1, 2025
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
Jan 1, 2025
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.)
Jan 1, 2025
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.)
Jan 1, 2025
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
Nov 27, 2024
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
Sep 23, 2024
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 ...
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