Extending the mutual information measure to rank inferred literature relationships

Background: Within the peer-reviewed literature, associations between two things are not always recognized until commonalities between them become apparent. These commonalities can provide justification for the inference of a new relationship where none was previously known, and are the basis of most observation-based hypothesis formation. It has been shown that the crux of the problem is not finding inferable associations, which are extraordinarily abundant given the scalefree networks that arise from literature-based associations, but determining which ones are informative. The Mutual Information Measure (MIM) is a well-established method to measure how informative an association is, but is limited to direct (i.e. observable) associations. Results: Herein, we attempt to extend the calculation of mutual information to indirect (i.e. inferable) associations by using the MIM of shared associations. Objects of general research interest (e.g. genes, diseases, phenotypes, drugs, ontology ...

• Type: Journal
• Year: 2004
• Where: BMCBI
• Authors: Jonathan D. Wren
• Keywords: BMCBI 2004, Literature-based Associations, Mutual Information, Observation-based Hypothesis Formation