Abstract
Background. EpiphaNet is an interactive knowledge discovery system which enables researchers to explore visually sets of relations extracted from MEDLINE using a combination of language processing techniques. In this paper, we discuss the theoretical and methodological foundations of the system, and evaluate the utility of the models that underlie it for literature-based discovery. In addition, we present a summary of results drawn from a qualitative analysis of over six hours of interaction with the system by basic medical scientists. The system is able to simulate open and closed discovery, and is shown to generate associations that are both surprising and interesting within the area of expertise of the researchers concerned. EpiphaNet provides an interactive visual representation of associations between concepts, which is derived from distributional statistics drawn from across the spectrum of biomedical citations in MEDLINE. This tool is available online, providing biomedical scientists with the opportunity to identify and explore associations of interest to them.
Highlights
The field of literature‐based discovery (LBD) emerged from a therapeutically useful connection between Raynaud's phenomenon and dietary fish oil discovered by Don Swanson [1]
This paper presents the theoretical and methodological underpinnings of EpiphaNet, a tool that harnesses the computational power of recent advances in distributional semantics [9] to encourage innovation by allowing scientists to explore associations they would not otherwise encounter
The attention of a domain expert is required to distinguish between those associations that are merely meaningful, and those that are surprising enough to indicate a path to discovery
Summary
The field of literature‐based discovery (LBD) emerged from a therapeutically useful connection between Raynaud's phenomenon and dietary fish oil discovered by Don Swanson [1]. Conclusions: EpiphaNet provides an interactive visual representation of associations between concepts, which is derived from distributional statistics drawn from across the spectrum of biomedical citations in MEDLINE This tool is available online, providing biomedical scientists with the opportunity to identify and explore associations of interest to them. 2.1 Literature‐based Knowledge Discovery The potential of undiscovered knowledge in the scientific literature was illustrated by the discovery of a previously unrecognized and therapeutically useful connection between fish oil and Raynaud's Syndrome by Don Swanson [1]. This literature was searched for article titles that did not include any reference to Raynaud's, revealing that blood viscosity was reduced, and red cell deformability increased, by dietary fish oil Swanson describes these articles as "logically connected", in that they are linked by an implicit scientific argument [10]. This scheme allows for two modes of discovery, which have been termed “open” and “closed" [11]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
