A comparative analysis of phenotypes derived from genes or biomedical literature in COVID-19

Sophie Steenson, Christopher Hawthorne, Guillermo Lopez-Campos

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

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Abstract

Since the emergence of SARS-CoV-2 in November 2019, there has been an exponential production of literature due to worldwide efforts to understand the interactions between the virus and the human body. Using an "in-house" developed script we retrieved gene annotations and identified phenotype enrichments. Human Phenotype Ontology terms were retrieved from the literature using the Onassis R package. This produced both disease-gene and disease-phenotype data as well as data for gene-phenotype interactions. Overall, we retrieved 181 human phenotypes that were identified by both approaches. Further in-depth analysis of these relationships could provide further insights in the molecular mechanisms related with the observed phenotypes, answers and hypotheses for key concepts within COVID-19 research.
Original languageEnglish
Title of host publicationMEDINFO 2021: One world, one health – global partnership for digital innovation
EditorsPaula Otero, Philip Scott, Susan Z. Martin, Elaine Huesing
PublisherIOS Press
Pages1092-1093
Number of pages2
ISBN (Electronic)9781643682655
ISBN (Print)9781643682648
DOIs
Publication statusPublished - 06 Jun 2022

Publication series

NameStudies in Health Technology and Informatics
Volume290
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Keywords

  • COVID-19
  • Computational Biology
  • SARS-CoV-2 - genetics
  • Phenotype
  • Humans
  • Data Mining

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