About: Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study
Creator	De Souza, Camila Hill, Kathleen Kari, Lila Randhawa, Gurjit El, Hadi Id, Roz Soltysiak, Maximillian
source	Medline; PMC
abstract	The 2019 novel coronavirus (renamed SARS-CoV-2, and generally referred to as the COVID-19 virus) has spread to 184 countries with over 1.5 million confirmed cases. Such major viral outbreaks demand early elucidation of taxonomic classification and origin of the virus genomic sequence, for strategic planning, containment, and treatment. This paper identifies an intrinsic COVID-19 virus genomic signature and uses it together with a machine learning-based alignment-free approach for an ultra-fast, scalable, and highly accurate classification of whole COVID-19 virus genomes. The proposed method combines supervised machine learning with digital signal processing (MLDSP) for genome analyses, augmented by a decision tree approach to the machine learning component, and a Spearman’s rank correlation coefficient analysis for result validation. These tools are used to analyze a large dataset of over 5000 unique viral genomic sequences, totalling 61.8 million bp, including the 29 COVID-19 virus sequences available on January 27, 2020. Our results support a hypothesis of a bat origin and classify the COVID-19 virus as Sarbecovirus, within Betacoronavirus. Our method achieves 100% accurate classification of the COVID-19 virus sequences, and discovers the most relevant relationships among over 5000 viral genomes within a few minutes, ab initio, using raw DNA sequence data alone, and without any specialized biological knowledge, training, gene or genome annotations. This suggests that, for novel viral and pathogen genome sequences, this alignment-free whole-genome machine-learning approach can provide a reliable real-time option for taxonomic classification.
has issue date	2020-04-24(xsd:dateTime)
bibo:doi	10.1371/journal.pone.0232391
bibo:pmid	32330208
has license	cc-by
sha1sum (hex)	1ae0ee249c39ecda3d9b4cbdfd491f83aff1e147
schema:url	https://doi.org/10.1371/journal.pone.0232391
resource representing a document's title	Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study
has PubMed Central identifier	PMC7182198
has PubMed identifier	32330208
schema:publication	PLoS One
resource representing a document's body	covid:1ae0ee249c39ecda3d9b4cbdfd491f83aff1e147#body_text
is schema:about of	named entity 'DEMAND' named entity 'CLASSIFICATION' named entity '27S' named entity 'FAST' named entity 'INTRINSIC' named entity 'SARS-COV-2' named entity 'MAJOR' named entity 'PROVIDE' named entity 'DIGITAL' named entity 'FEW MINUTES' named entity 'CLASSIFY' named entity 'REAL-TIME' covid:arg/1ae0ee249c39ecda3d9b4cbdfd491f83aff1e147 named entity 'genomic sequences' named entity 'machine-learning approach' named entity 'Sarbecovirus' named entity 'COVID-19 virus' named entity 'gene' named entity '1.5 million' named entity 'viral genomes' named entity 'COVID-19 virus' named entity 'COVID-19 virus' named entity 'genome sequences' named entity 'supervised machine learning' named entity 'kbp' named entity 'phylogenetic trees' named entity 'Anelloviridae' named entity 'genomes' named entity 'CGR' named entity 'neighbor-joining' named entity 'Gammacoronavirus' named entity 'Sarbecovirus' named entity 'Sarbecovirus' named entity 'bat' named entity 'UPGMA' named entity 'machine learning' named entity 'phylogenetic trees' named entity 'Coronaviridae' named entity 'Sarbecovirus' named entity 'protein' named entity 'SARS' named entity 'COVID-19 virus' named entity 'minimum number' named entity 'COVID-19 virus' named entity 'recombination' named entity 'HIV-1' named entity 'gene' named entity 'genomic sequences' named entity 'Betacoronavirus' named entity 'genome' named entity 'taxonomic ranks' named entity 'Riboviria' named entity 'taxonomic classification' named entity 'COVID-19 virus' named entity 'Polydnaviridae' named entity 'Linear Discriminant' named entity 'k-mer' named entity 'mammals' named entity 'COVID-19 virus' named entity 'RNA' named entity 'Reoviridae' named entity 'gene' named entity 'prevent the spread' named entity 'mammals' named entity 'genus' named entity 'virus classification' named entity 'comparative genomics' named entity 'bat' named entity 'genome comparison' named entity 'bat' named entity 'COVID-19 virus' named entity 'taxonomic classification' named entity 'non-self' named entity 'MERS-CoV' named entity 'COVID-19 virus'

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