Shared relationship analysis: ranking set cohesion and commonalities within a literature-derived relationship network.
BACKGROUND
There is a general scientific need to be able to identify and evaluate what any given set of 'objects' (e.g. genes, phenotypes, chemicals, diseases) has in common. Whether it is to classify, expand upon or identify commonalities and functional groupings, informational needs can be diverse and the best source to identify relationships among a potentially heterogeneous set of objects is the scientific literature.


RESULTS
We first establish a network of related objects by their co-occurrence within MEDLINE records. A set of objects within this network can then be queried to identify shared relationships, and a method is presented to score their statistical relevance by comparing observed frequencies with what would be expected in a random network model. Using Gene Ontology (GO) categories, we demonstrate that this method enables a quantitative ranking of the 'cohesiveness' of a set of objects and, importantly, allows other objects related to this set to be identified and evaluated for their 'cohesion' to it. Supplemental information: A list of ranked genes related to each GO category analyzed can be found at http://innovation.swmed.edu/IRIDESCENT/GO_relationships.htm
Wren
Jonathan D
JD
Advanced Center for Genome Technology, Department of Botany and Microbiology, The University of Oklahoma, 620 Parrington Oval, Rm. 106, Norman, OK 73019, USA. Jonathan.Wren@OU.edu
Garner
Harold R
HR
D000043:Abstracting and Indexing; D000465:Algorithms; D001185:Artificial Intelligence; D003628:Database Management Systems; D030541:Databases, Genetic; D016247:Information Storage and Retrieval / Q000379:methods*; D016239:MEDLINE; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated; D010506:Periodicals as Topic; D009626:Terminology as Topic