About: A Network Integration Approach to Predict Conserved Regulators Related to Pathogenicity of Influenza and SARS-CoV Respiratory Viruses

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About: A Network Integration Approach to Predict Conserved Regulators Related to Pathogenicity of Influenza and SARS-CoV Respiratory Viruses Goto Sponge NotDistinct Permalink

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
has title	A Network Integration Approach to Predict Conserved Regulators Related to Pathogenicity of Influenza and SARS-CoV Respiratory Viruses
Creator	Baric, Ralph Sims, Amy Kawaoka, Yoshihiro Chang, Jean Josset, Laurence Tchitchek, Nicolas Li, Chengjun Neumann, Gabriele Matzke, Melissa Smith, Richard Benecke, Arndt Ellis, Amy Mitchell, Hugh Eisfeld, Amie Katze, Michael Metz, Thomas Schepmoes, Athena Shukla, Anil Waters, Katrina Heegel, Robert Luna, Maria Mcdermott, Jason Tilton, Susan Webb-Robertson, Bobbi-Jo
Source	Medline; PMC
abstract	Respiratory infections stemming from influenza viruses and the Severe Acute Respiratory Syndrome corona virus (SARS-CoV) represent a serious public health threat as emerging pandemics. Despite efforts to identify the critical interactions of these viruses with host machinery, the key regulatory events that lead to disease pathology remain poorly targeted with therapeutics. Here we implement an integrated network interrogation approach, in which proteome and transcriptome datasets from infection of both viruses in human lung epithelial cells are utilized to predict regulatory genes involved in the host response. We take advantage of a novel “crowd-based” approach to identify and combine ranking metrics that isolate genes/proteins likely related to the pathogenicity of SARS-CoV and influenza virus. Subsequently, a multivariate regression model is used to compare predicted lung epithelial regulatory influences with data derived from other respiratory virus infection models. We predicted a small set of regulatory factors with conserved behavior for consideration as important components of viral pathogenesis that might also serve as therapeutic targets for intervention. Our results demonstrate the utility of integrating diverse ‘omic datasets to predict and prioritize regulatory features conserved across multiple pathogen infection models.
has issue date	2013-07-25(xsd:dateTime)
bibo:doi	10.1371/journal.pone.0069374
bibo:pmid	23935999
has license	cc0
sha1sum (hex)	ccaafc13b2c1551dce74e1a7fe3e43cea5228732
schema:url	https://doi.org/10.1371/journal.pone.0069374
resource representing a document's title	A Network Integration Approach to Predict Conserved Regulators Related to Pathogenicity of Influenza and SARS-CoV Respiratory Viruses
has PubMed Central identifier	PMC3723910
has PubMed identifier	23935999
schema:publication	PLoS One
resource representing a document's body	covid:ccaafc13b2c1551dce74e1a7fe3e43cea5228732#body_text
is schema:about of	named entity 'small set' named entity 'network' named entity 'events' named entity 'conserved' named entity 'viruses' named entity 'Respiratory' named entity 'Regulators' named entity 'INTEGRATION' named entity 'INFLUENZA' named entity 'INTERACTIONS' named entity 'SMALL' named entity 'RESPIRATORY VIRUSES' named entity 'RESPIRATORY VIRUSES' named entity 'KEY' named entity 'ISOLATE' named entity 'TRANSCRIPTOME' named entity 'RESULTS' named entity 'OUR' named entity 'ADVANTAGE' named entity 'USED' named entity 'TARGETS' named entity 'CRITICAL' named entity 'REGULATORS' named entity 'TO COMPARE' named entity 'INTEGRATING' named entity 'EVENTS' named entity 'PATHOGENICITY' named entity 'GENES' named entity 'INFLUENZA VIRUSES' named entity 'TO IDENTIFY' named entity 'RESPIRATORY INFECTIONS' named entity 'INTEGRATION' named entity 'PREDICTED' named entity 'INTERROGATION' named entity 'PATHOGEN' named entity 'CONSERVED' named entity 'MACHINERY' named entity 'RANKING' named entity 'SIMS' named entity 'DIVERSE' named entity 'PREDICT' named entity 'PATHOGENICITY' named entity 'SARS-COV' named entity 'RELATED' named entity 'REGULATORS' named entity 'MULTIVARIATE REGRESSION MODEL' named entity 'DISEASE' named entity 'SET' named entity 'CORONA' named entity 'THERAPEUTICS' named entity 'BEHAVIOR' named entity 'APPROACH' named entity 'REGULATORY' named entity 'METRICS' named entity 'HUMAN LUNG' named entity 'NOVEL' named entity 'INFLUENZA VIRUS' named entity '287'

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