AIMC Topic: Host Specificity

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

Nature communications May 22, 2024
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...
Computer Simulation Klebsiella Bacteriophages Host Specificity Machine Learning
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Divide-and-conquer: machine-learning integrates mammalian and viral traits with network features to predict virus-mammal associations.

Nature communications Jun 25, 2021
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...
Reproducibility of Results Animals Mammals Virus Diseases Humans Machine Learning Virus Physiological Phenomena Host Specificity Viruses Zoonoses Disease Reservoirs
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Analysis of protein determinants of host-specific infection properties of polyomaviruses using machine learning.

Genes & genomics Mar 1, 2021
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).
Capsid Proteins Host Specificity Antigens, Polyomavirus Transforming Machine Learning Phylogeny Amino Acids Polyomavirus
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Machine learning methods accurately predict host specificity of coronaviruses based on spike sequences alone.

Biochemical and biophysical research communications Sep 18, 2020
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...
Machine Learning Animals Spike Glycoprotein, Coronavirus Computational Biology Host Specificity Coronavirus Humans
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Using mid-infrared spectroscopy and supervised machine-learning to identify vertebrate blood meals in the malaria vector, Anopheles arabiensis.

Malaria journal May 30, 2019
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...
Spectrophotometry, Infrared Host Specificity Logistic Models Vertebrates Blood Chickens Anopheles Malaria Humans Feeding Behavior Female Mosquito Vectors Animals Goats Supervised Machine Learning
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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...
Phylogeny Bacterial Proteins Mutation Virulence Salmonella enterica Salmonella Infections Humans Adaptation, Physiological Machine Learning Host Specificity Animals Salmonella Infections, Animal
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Patchy promiscuity: machine learning applied to predict the host specificity of and .

Microbial genomics Oct 3, 2017
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...
Zoonoses Genomic Library Host Specificity Animals Escherichia coli Humans Phylogeny Salmonella enterica Machine Learning
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Predicting Zoonotic Risk of Influenza A Viruses from Host Tropism Protein Signature Using Random Forest.

International journal of molecular sciences May 25, 2017
Influenza A viruses remain a significant health problem, especially when a novel subtype emerges from the avian population to cause severe outbreaks in humans. Zoonotic viruses arise from the animal population as a result of mutations and reassortmen...
Orthomyxoviridae Infections Host Specificity Reproducibility of Results Humans Zoonoses Animals Influenza, Human Models, Theoretical Host-Pathogen Interactions Viral Tropism Viral Proteins Databases, Genetic Area Under Curve Disease Outbreaks Influenza in Birds Birds Influenza A virus Machine Learning Retrospective Studies
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MOSTPLAS: a self-correction multi-label learning model for plasmid host range prediction.

Bioinformatics (Oxford, England) Mar 4, 2025
MOTIVATION: Plasmids play an essential role in horizontal gene transfer, aiding their host bacteria in acquiring beneficial traits like antibiotic and metal resistance. There exist some plasmids that can transfer, replicate, or persist in multiple or...
Machine Learning Software Databases, Genetic Plasmids Gene Transfer, Horizontal Algorithms Host Specificity Bacteria Computational Biology
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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...
Algorithms Host Specificity Bacteria Support Vector Machine Machine Learning Plasmids Genome, Bacterial
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