AIMC Topic: Virulence
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Phenotype-Based Threat Assessment.
Proceedings of the National Academy of Sciences of the United States of America
Apr 1, 2022
Bacterial pathogen identification, which is critical for human health, has historically relied on culturing organisms from clinical specimens. More recently, the application of machine learning (ML) to whole-genome sequences (WGSs) has facilitated pa...
Identification of antibiotic resistance and virulence-encoding factors in Klebsiella pneumoniae by Raman spectroscopy and deep learning.
Microbial biotechnology
Nov 29, 2021
Klebsiella pneumoniae has become the number one bacterial pathogen that causes high mortality in clinical settings worldwide. Clinical K. pneumoniae strains with carbapenem resistance and/or hypervirulent phenotypes cause higher mortality comparing w...
Dual-Organ Transcriptomic Analysis of Rainbow Trout Infected With Through Co-Expression and Machine Learning.
Frontiers in immunology
Jul 8, 2021
is a major pathogen that causes a high mortality rate in trout farms. However, systemic responses to the pathogen and its interactions with multiple organs during the course of infection have not been well described. In this study, dual-organ transc...
Forest and Trees: Exploring Bacterial Virulence with Genome-wide Association Studies and Machine Learning.
Trends in microbiology
Jan 14, 2021
The advent of inexpensive and rapid sequencing technologies has allowed bacterial whole-genome sequences to be generated at an unprecedented pace. This wealth of information has revealed an unanticipated degree of strain-to-strain genetic diversity w...
SVFX: a machine learning framework to quantify the pathogenicity of structural variants.
Genome biology
Nov 9, 2020
There is a lack of approaches for identifying pathogenic genomic structural variants (SVs) although they play a crucial role in many diseases. We present a mechanism-agnostic machine learning-based workflow, called SVFX, to assign pathogenicity score...
Deep-Learning Resources for Studying Glycan-Mediated Host-Microbe Interactions.
Cell host & microbe
Oct 28, 2020
Glycans, the most diverse biopolymer, are shaped by evolutionary pressures stemming from host-microbe interactions. Here, we present machine learning and bioinformatics methods to leverage the evolutionary information present in glycans to gain insig...
A Genome-Based Model to Predict the Virulence of Pseudomonas aeruginosa Isolates.
mBio
Aug 25, 2020
Variation in the genome of , an important pathogen, can have dramatic impacts on the bacterium's ability to cause disease. We therefore asked whether it was possible to predict the virulence of isolates based on their genomic content. We applied a m...
Compositional model based on factorial evolution for realizing multi-task learning in bacterial virulent protein prediction.
Artificial intelligence in medicine
Nov 7, 2019
The ability of multitask learning promulgated its sovereignty in the machine learning field with the diversified application including but not limited to bioinformatics and pattern recognition. Bioinformatics provides a wide range of applications for...
Machine Learning Methods as a Tool for Predicting Risk of Illness Applying Next-Generation Sequencing Data.
Risk analysis : an official publication of the Society for Risk Analysis
Nov 21, 2018
Next-generation sequencing (NGS) data present an untapped potential to improve microbial risk assessment (MRA) through increased specificity and redefinition of the hazard. Most of the MRA models do not account for differences in survivability and vi...
Machine learning identifies signatures of host adaptation in the bacterial pathogen Salmonella enterica.
PLoS genetics
May 8, 2018
Emerging pathogens are a major threat to public health, however understanding how pathogens adapt to new niches remains a challenge. New methods are urgently required to provide functional insights into pathogens from the massive genomic data sets no...
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