A text mining approach to explore IFNε literature and biological mechanisms

Mary McCabe; Helen E Groves; Ultan F Power; Guillermo Lopez Campos

doi:10.3233/SHTI231122

A text mining approach to explore IFNε literature and biological mechanisms

Mary McCabe, Helen E Groves, Ultan F Power, Guillermo Lopez Campos

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

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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 language	English
Title of host publication	MEDINFO 2023 — The Future Is Accessible
Editors	Jen Bichel-Findlay, Paula Otero, Philip Scott, Elaine Huesing
Publisher	IOS Press
Pages	1036-1040
Number of pages	5
ISBN (Electronic)	9781643684574
ISBN (Print)	9781643684567
DOIs	https://doi.org/10.3233/SHTI231122
Publication status	Published - 25 Jan 2024

Publication series

Name	Studies in Health Technology and Informatics
Publisher	IOS Press
Volume	310
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Keywords

Data Mining
Knowledge

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10.3233/SHTI231122Licence: CC BY-NC

A text mining approach to explore IFNε literature and biological mechanisms
© 2024 International Medical Informatics Association (IMIA) and IOS Press This is an open access Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits use, distribution and reproduction for non-commercial purposes, provided the author and source are cited.
Final published version, 520 KBLicence: CC BY-NC

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McCabe, M., Groves, H. E., Power, U. F., & Lopez Campos, G. (2024). A text mining approach to explore IFNε literature and biological mechanisms. In J. Bichel-Findlay, P. Otero, P. Scott, & E. Huesing (Eds.), MEDINFO 2023 — The Future Is Accessible (pp. 1036-1040). (Studies in Health Technology and Informatics; Vol. 310). IOS Press. https://doi.org/10.3233/SHTI231122

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A text mining approach to explore IFNε literature and biological mechanisms. / McCabe, Mary ; Groves, Helen E ; Power, Ultan F et al.
MEDINFO 2023 — The Future Is Accessible. ed. / Jen Bichel-Findlay; Paula Otero; Philip Scott; Elaine Huesing. IOS Press, 2024. p. 1036-1040 (Studies in Health Technology and Informatics; Vol. 310).

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

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AB - 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.

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KW - Knowledge

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A text mining approach to explore IFNε literature and biological mechanisms

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