Selected article for: "cellular network and integrated cellular network"
Author: O’Donovan, Sinead M; Eby, Hunter; Henkel, Nicholas D; Creeden, Justin; Imami, Ali; Asah, Sophie; Zhang, Xiaolu; Wu, Xiaojun; Alnafisah, Rawan; Taylor, R. Travis; Reigle, James; Thorman, Alexander; Shamsaei, Behrouz; Meller, Jarek; McCullumsmith, Robert E
Title: Identification of new drug treatments to combat COVID19: A signature-based approach using iLINCS
  • Cord-id: jvenua7z
  • Document date: 2020_4_30
    • ID: jvenua7z
    Snippet: The COVID-19 pandemic caused by the novel SARS-CoV-2 is more contagious than other coronaviruses and has higher rates of mortality than influenza. As no vaccine or drugs are currently approved to specifically treat COVID-19, identification of effective therapeutics is crucial to treat the afflicted and limit disease spread. We deployed a bioinformatics workflow to identify candidate drugs for the treatment of COVID-19. Using an “omics” repository, the Library of Integrated Network-Based Cell
    Document: The COVID-19 pandemic caused by the novel SARS-CoV-2 is more contagious than other coronaviruses and has higher rates of mortality than influenza. As no vaccine or drugs are currently approved to specifically treat COVID-19, identification of effective therapeutics is crucial to treat the afflicted and limit disease spread. We deployed a bioinformatics workflow to identify candidate drugs for the treatment of COVID-19. Using an “omics” repository, the Library of Integrated Network-Based Cellular Signatures (LINCS), we simultaneously probed transcriptomic signatures of putative COVID-19 drugs and signatures of coronavirus-infected cell lines to identify therapeutics with concordant signatures and discordant signatures, respectively. Our findings include three FDA approved drugs that have established antiviral activity, including protein kinase inhibitors, providing a promising new category of candidates for COVID-19 interventions.

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