A text mining approach to explore IFNε literature and biological mechanisms

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Abstract

Interferons (IFN) constitute a primary line of protection against mucosal infection, with IFN research spanning over 60 years and encompassing a vast ever-expanding amount of literature. Most of what is currently understood has been derived from extensive research defining the roles of "classical" type I IFNs, IFNα and IFNβ. However, little is known regarding responses elicited by less well-characterized IFN subtypes such as IFNε. In this paper, we combined a deductive text mining analysis of IFNε literature characterizing literature-derived knowledge with a comparative analysis of other type I and type III IFNs. Utilizing these approaches, three clusters of terms were extracted from the literature covering different aspects of IFNε research and a set of 47 genes uniquely cited in the context of IFNε. The use of these "in silico" approaches support the expansion of current understanding and the creation of new knowledge surrounding IFNε.

Original languageEnglish
Title of host publicationMEDINFO 2023 — The Future Is Accessible
EditorsJen Bichel-Findlay, Paula Otero, Philip Scott, Elaine Huesing
PublisherIOS Press
Pages1036-1040
Number of pages5
ISBN (Electronic)9781643684574
ISBN (Print)9781643684567
DOIs
Publication statusPublished - 25 Jan 2024

Publication series

NameStudies in Health Technology and Informatics
PublisherIOS Press
Volume310
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Keywords

  • Data Mining
  • Knowledge

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