AIMC Topic: Virulence Factors

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Accurate prediction of virulence factors using pre-train protein language model and ensemble learning.

BMC genomics
BACKGROUND: As bacterial pathogens develop increasing resistance to antibiotics, strategies targeting virulence factors (VFs) have emerged as a promising and effective approach for treating bacterial infections. Existing methods mainly relied on sequ...

Using core genome and machine learning for serovar prediction in Salmonella enterica subspecies I strains.

FEMS microbiology letters
This study presents a dual investigation of Salmonella enterica subspecies I, focusing on serovar prediction and core genome characteristics. We utilized two large genomic datasets (panX and NCBI Pathogen Detection) to test machine learning methods f...

Highly accurate classification and discovery of microbial protein-coding gene functions using FunGeneTyper: an extensible deep learning framework.

Briefings in bioinformatics
High-throughput DNA sequencing technologies decode tremendous amounts of microbial protein-coding gene sequences. However, accurately assigning protein functions to novel gene sequences remain a challenge. To this end, we developed FunGeneTyper, an e...

Mechanism of assembly of type 4 filaments: everything you always wanted to know (but were afraid to ask).

Microbiology (Reading, England)
Type 4 filaments (T4F) are a superfamily of filamentous nanomachines - virtually ubiquitous in prokaryotes and functionally versatile - of which type 4 pili (T4P) are the defining member. T4F are polymers of type 4 pilins, assembled by conserved mult...

Prediction of prokaryotic transposases from protein features with machine learning approaches.

Microbial genomics
Identification of prokaryotic transposases (Tnps) not only gives insight into the spread of antibiotic resistance and virulence but the process of DNA movement. This study aimed to develop a classifier for predicting Tnps in bacteria and archaea usin...

DeepVF: a deep learning-based hybrid framework for identifying virulence factors using the stacking strategy.

Briefings in bioinformatics
Virulence factors (VFs) enable pathogens to infect their hosts. A wealth of individual, disease-focused studies has identified a wide variety of VFs, and the growing mass of bacterial genome sequence data provides an opportunity for computational met...

Predicting bacterial virulence factors - evaluation of machine learning and negative data strategies.

Briefings in bioinformatics
Bacterial proteins dubbed virulence factors (VFs) are a highly diverse group of sequences, whose only obvious commonality is the very property of being, more or less directly, involved in virulence. It is therefore tempting to speculate whether their...

Learning transferable deep convolutional neural networks for the classification of bacterial virulence factors.

Bioinformatics (Oxford, England)
MOTIVATION: Identification of virulence factors (VFs) is critical to the elucidation of bacterial pathogenesis and prevention of related infectious diseases. Current computational methods for VF prediction focus on binary classification or involve on...

Victors: a web-based knowledge base of virulence factors in human and animal pathogens.

Nucleic acids research
Virulence factors (VFs) are molecules that allow microbial pathogens to overcome host defense mechanisms and cause disease in a host. It is critical to study VFs for better understanding microbial pathogenesis and host defense mechanisms. Victors (ht...