AIMC Topic: Host Specificity

Clear Filters Showing 1 to 10 of 15 articles

A machine learning approach to predict strain-specific phage-host interactions.

Scientific reports
The use of bacteriophages for biological control of bacterial infections is a promising approach to combat antimicrobial resistant bacteria. Prediction of phage-bacteria interactions is key to identify sensitive bacterial strains to phage therapy. Si...

Viral interferon antagonism shapes host tropism.

PLoS pathogens
Outbreaks of zoonotic viruses in human populations highlight the need to understand the molecular factors that influence viral host tropism and interspecies transmission. A virus's host range is determined not only by the expression of host factors t...

Flu-CNN: identifying host specificity of Influenza A virus using convolutional networks.

Human genomics
Influenza A viruses (IAVs) have historically posed significant public health threats, causing severe pandemics. Viral host specificity is typically constrained by host barriers, limiting the range of species that can be infected. However, these barri...

Prediction of Klebsiella phage-host specificity at the strain level.

Nature communications
Phages are increasingly considered promising alternatives to target drug-resistant bacterial pathogens. However, their often-narrow host range can make it challenging to find matching phages against bacteria of interest. Current computational tools d...

Divide-and-conquer: machine-learning integrates mammalian and viral traits with network features to predict virus-mammal associations.

Nature communications
Our knowledge of viral host ranges remains limited. Completing this picture by identifying unknown hosts of known viruses is an important research aim that can help identify and mitigate zoonotic and animal-disease risks, such as spill-over from anim...

Analysis of protein determinants of host-specific infection properties of polyomaviruses using machine learning.

Genes & genomics
BACKGROUND: The large tumor antigen (LT-Ag) and major capsid protein VP1 are known to play important roles in determining the host-specific infection properties of polyomaviruses (PyVs).

Machine learning methods accurately predict host specificity of coronaviruses based on spike sequences alone.

Biochemical and biophysical research communications
Coronaviruses infect many animals, including humans, due to interspecies transmission. Three of the known human coronaviruses: MERS, SARS-CoV-1, and SARS-CoV-2, the pathogen for the COVID-19 pandemic, cause severe disease. Improved methods to predict...

Using mid-infrared spectroscopy and supervised machine-learning to identify vertebrate blood meals in the malaria vector, Anopheles arabiensis.

Malaria journal
BACKGROUND: The propensity of different Anopheles mosquitoes to bite humans instead of other vertebrates influences their capacity to transmit pathogens to humans. Unfortunately, determining proportions of mosquitoes that have fed on humans, i.e. Hum...

Machine learning identifies signatures of host adaptation in the bacterial pathogen Salmonella enterica.

PLoS genetics
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...

Patchy promiscuity: machine learning applied to predict the host specificity of and .

Microbial genomics
and are bacterial species that colonize different animal hosts with sub-types that can cause life-threatening infections in humans. Source attribution of zoonoses is an important goal for infection control as is identification of isolates in reserv...