PropertyValue
?:abstract
  • The stratification of patients at risk of progression of COVID-19 and their molecular characterization is of extreme importance to optimize treatment and to identify therapeutic options. The bioinformatics community has responded to the outbreak emergency with a set of tools and resource to identify biomarkers and drug targets that we review here. Starting from a consolidated corpus of 27 570 papers, we adopt latent Dirichlet analysis to extract relevant topics and select those associated with computational methods for biomarker identification and drug repurposing. The selected topics span from machine learning and artificial intelligence for disease characterization to vaccine development and to therapeutic target identification. Although the way to go for the ultimate defeat of the pandemic is still long, the amount of knowledge, data and tools generated so far constitutes an unprecedented example of global cooperation to this threat.
is ?:annotates of
?:creator
?:doi
?:doi
  • 10.1093/bib/bbaa328
?:journal
  • Brief_Bioinform
?:license
  • cc-by-nc
?:pdf_json_files
  • document_parses/pdf_json/63b526ff311c23f32f7d61b6363e070e854fe894.json
?:pmc_json_files
  • document_parses/pmc_json/PMC7799271.xml.json
?:pmcid
?:pmid
?:pmid
  • 33279954.0
?:publication_isRelatedTo_Disease
?:sha_id
?:source
  • Medline; PMC
?:title
  • A review of COVID-19 biomarkers and drug targets: resources and tools
?:type
?:year
  • 2020-12-07

Metadata

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