Article

(Machine) Learning the mutation signatures of SARS-CoV-2: a primer for predictive prognosis

2021-08-31

Abstract excerpt

<h4>Motivation</h4> Continuous emergence of new variants through appearance, accumulation and disappearance of mutations in viruses is a hallmark of many viral diseases. SARS-CoV-2 and its variants have particularly exerted tremendous pressure on global healthcare system owing to their life threatening and debilitating implications. The sheer plurality of the variants and huge scale of genome sequence data availa...

Topics

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

Identifiers and source

Literature Corpus work: 8ead1483-09ba-5143-98af-caf0739a3f27
DOI: 10.1101/2021.08.30.458244

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

(Machine) Learning the mutation signatures of SARS-CoV-2: a primer for predictive prognosisDOI 10.1101/2021.08.30.458244Read

Can machines learn the mutation signatures of SARS-CoV-2 and enable viral-genotype guided predictive prognosis?Semantic proximity Read

COVIDOUTCOME – Estimating COVID Severity Based on Mutation Signatures in the SARS-CoV-2 GenomeSemantic proximity Read

COVIDOUTCOME-estimating COVID severity based on mutation signatures in the SARS-CoV-2 genome.Semantic proximity Read

Learning From Biological and Computational Machines: Importance of SARS-CoV-2 Genomic Surveillance, Mutations and Risk Stratification.Semantic proximity Read

Identifying COVID-19 Severity-Related SARS-CoV-2 Mutation Using a Machine Learning MethodSemantic proximity Read

CovidOutcome2: a tool for SARS-CoV2 mutation identification and for disease severity predictionSemantic proximity Read

Genome-wide identification and prediction of SARS-CoV-2 mutations show an abundance of variants: Integrated study of bioinformatics and deep neural learningSemantic proximity Read

SARS-CoV-2: lessons in virus mutation prediction and pandemic preparednessSemantic proximity Read

Genome-wide identification and prediction of SARS-CoV-2 mutations show an abundance of variants: Integrated study of bioinformatics and deep neural learningSemantic proximity Read

Enhanced predictability and interpretability of COVID-19 severity based on SARS-CoV-2 genomic diversity: a comprehensive study encompassing four years of data.Semantic proximity Read

Emerging SARS-CoV-2 mutation hotspots associated with clinical outcomesSemantic proximity Read

Predicting the mutational drivers of future SARS-CoV-2 variants of concern.Semantic proximity Read