AIMC Topic: Genome, Bacterial
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ProPr54 web server: predicting σ promoters and regulon with a hybrid convolutional and recurrent deep neural network.
NAR genomics and bioinformatics
Jan 7, 2025
σ serves as an unconventional sigma factor with a distinct mechanism of transcription initiation, which depends on the involvement of a transcription activator. This unique sigma factor σ is indispensable for orchestrating the transcription of genes ...
Using genomic data and machine learning to predict antibiotic resistance: A tutorial paper.
PLoS computational biology
Dec 30, 2024
Antibiotic resistance is a global public health concern. Bacteria have evolved resistance to most antibiotics, which means that for any given bacterial infection, the bacteria may be resistant to one or several antibiotics. It has been suggested that...
Towards AI-designed genomes using a variational autoencoder.
Proceedings. Biological sciences
Dec 11, 2024
Genomes encode elaborate networks of genes whose products must seamlessly interact to support living organisms. Humans' capacity to understand these biological systems is limited by their sheer size and complexity. In this article, we develop a proof...
Neural network-based predictions of antimicrobial resistance phenotypes in multidrug-resistant from whole genome sequencing and gene expression.
Antimicrobial agents and chemotherapy
Nov 14, 2024
Whole genome sequencing (WGS) potentially represents a rapid approach for antimicrobial resistance genotype-to-phenotype prediction. However, the challenge still exists to predict fully minimum inhibitory concentrations (MICs) and antimicrobial susce...
Leveraging large-scale Mycobacterium tuberculosis whole genome sequence data to characterise drug-resistant mutations using machine learning and statistical approaches.
Scientific reports
Nov 7, 2024
Tuberculosis disease (TB), caused by Mycobacterium tuberculosis (Mtb), is a major global public health problem, resulting in > 1 million deaths each year. Drug resistance (DR), including the multi-drug form (MDR-TB), is challenging control of the dis...
sRNAdeep: a novel tool for bacterial sRNA prediction based on DistilBERT encoding mode and deep learning algorithms.
BMC genomics
Oct 31, 2024
BACKGROUND: Bacterial small regulatory RNA (sRNA) plays a crucial role in cell metabolism and could be used as a new potential drug target in the treatment of pathogen-induced disease. However, experimental methods for identifying sRNAs still require...
CSEL-BGC: A Bioinformatics Framework Integrating Machine Learning for Defining the Biosynthetic Evolutionary Landscape of Uncharacterized Antibacterial Natural Products.
Interdisciplinary sciences, computational life sciences
Sep 30, 2024
The sluggish pace of new antibacterial drug development reflects a vulnerability in the face of the current severe threat posed by bacterial resistance. Microbial natural products (NPs), as a reservoir of immense chemical potential, have emerged as t...
Prediction of antimicrobial resistance of Klebsiella pneumoniae from genomic data through machine learning.
PloS one
Sep 18, 2024
Antimicrobials, such as antibiotics or antivirals are medications employed to prevent and treat infectious diseases in humans, animals, and plants. Antimicrobial Resistance occurs when bacteria, viruses, and parasites no longer respond to these medic...
Machine learning to attribute the source of Campylobacter infections in the United States: A retrospective analysis of national surveillance data.
The Journal of infection
Sep 7, 2024
OBJECTIVES: Integrating pathogen genomic surveillance with bioinformatics can enhance public health responses by identifying risk and guiding interventions. This study focusses on the two predominant Campylobacter species, which are commonly found in...
Holistic understanding of trimethoprim resistance in using an integrative approach of genome-wide association study, resistance reconstruction, and machine learning.
mBio
Aug 9, 2024
UNLABELLED: Antimicrobial resistance (AMR) is a public health threat worldwide. Next-generation sequencing (NGS) has opened unprecedented opportunities to accelerate AMR mechanism discovery and diagnostics. Here, we present an integrative approach to...
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