A Text Mining Approach to Explore IFNε Literature and Biological Mechanisms

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.

Keywords: Text-mining; bioinformatics; interferon epsilon; interferons.