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. 2010 Sep 21:5:21-49.

EpiphaNet: An Interactive Tool to Support Biomedical Discoveries

Trevor Cohen  1 , G Kerr Whitfield, Roger W Schvaneveldt, Kavitha Mukund, Thomas Rindflesch
Affiliations

EpiphaNet: An Interactive Tool to Support Biomedical Discoveries

Trevor Cohen et al. J Biomed Discov Collab. .

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.

Results: 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.

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.

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Figures

Figure 1:
Figure 1:
Open and closed literature-based discovery. In both cases, the starting point or points for the discovery process have thick borders, and the endpoint is surrounded by a starburst.
Figure 2
Figure 2
General associations of the terms “merlot” (left), and “mrsa” (right) captured from the current web-based edition of EpiphaNet.
Figure 3
Figure 3
Specific associations of the UMLS concept “wine” and “methicillin resistance”.
Figure 4
Figure 4
Excerpt of network linking search concepts “amitryptiline” and “prozac”.
Figure 5
Figure 5
Second order relations of the concepts “wine” and “methicillin-resistance” generated using the logical leaps feature of EpiphaNet.
Figure 6
Figure 6
Open and closed LBD using EpiphaNet. In both cases, the starting point or points for the discovery process have thick borders, and the endpoint is surrounded by a starburst.
Figure 7
Figure 7
Predictive ability of RRI, PSI and random vectors (Mean +- SEM, N=500).
Figure 8
Figure 8
Association strength and predictive ability.
Table 1
Table 1
This table presents a summary of the user activities during the ten sessions that are related to the discovery of new hypotheses.
Figure 9
Figure 9
Breakdown of EpiphaNet searches conducted in ten sessions. “NONE” = unconstrained searches retrieving general associations.
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