Article

Predicting the animal hosts of coronaviruses from compositional biases of spike protein and whole genome sequences through machine learning

2020-11-02

Abstract excerpt

The COVID-19 pandemic has demonstrated the serious potential for novel zoonotic coronaviruses to emerge and cause major outbreaks. The immediate animal origin of the causative virus, SARS-CoV-2, remains unknown, a notoriously challenging task for emerging disease investigations. Coevolution with hosts leads to specific evolutionary signatures within viral genomes that can inform likely animal origins. We obtained...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: da39934f-c779-5c27-8c61-1e19292ca0e9
DOI: 10.1101/2020.11.02.350439

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Predicting the animal hosts of coronaviruses from compositional biases of spike protein and whole genome sequences through machine learningDOI 10.1101/2020.11.02.350439Read

Predicting the animal hosts of coronaviruses from compositional biases of spike protein and whole genome sequences through machine learningSemantic proximity Read

Developing Foundation Models for Predicting Viral Animal Host Range in Intelligent SurveillanceSemantic proximity Read

Divide-and-conquer: machine-learning integrates mammalian and viral traits with network features to predict virus-mammal associationsSemantic proximity Read

Machine learning-based framework for predicting human infection potential of coronavirus associated with tri-Amino acid motifs, KIQ and LEP in spike proteinSemantic proximity Read

Machine learning methods accurately predict host specificity of coronaviruses based on spike sequences aloneSemantic proximity Read

Exploring genetic signatures of zoonotic influenza A virus at the swine-human interface with phylogenetic and ancestral sequence reconstructionSemantic proximity Read

A Genotype-to-Phenotype Modeling Framework to Predict Human Pathogenicity of Novel CoronavirusesSemantic proximity Read

Identification of potential SARS-CoV-2 genetic markers resulting from host domesticationSemantic proximity Read

Predicting reservoir hosts and arthropod vectors from evolutionary signatures in RNA virus genomesSemantic proximity Read

Metagenomic identification of a new sarbecovirus from horseshoe bats in EuropeSemantic proximity Read

Characterising alphacoronavirus phenotypic traits through diversity-driven selection of spikeSemantic proximity Read

Natural selection differences detected in key protein domains between non-pathogenic and pathogenic feline coronavirus phenotypesSemantic proximity Read