Jonathan D Wren1, Harold R Garner. 1. 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
Abstract
MOTIVATION: 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
MOTIVATION: 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
Authors: Siva K Gandhapudi; Chibing Tan; Julie H Marino; Ashlee A Taylor; Christopher C Pack; Joel Gaikwad; C Justin Van De Wiele; Jonathan D Wren; T Kent Teague Journal: J Immunol Date: 2015-03-16 Impact factor: 5.422
Authors: Pei-Suen Tsou; Jonathan D Wren; M Asif Amin; Elena Schiopu; David A Fox; Dinesh Khanna; Amr H Sawalha Journal: Arthritis Rheumatol Date: 2016-12 Impact factor: 10.995
Authors: Stine N Clemmensen; Christina T Bohr; Sara Rørvig; Andreas Glenthøj; Helena Mora-Jensen; Elisabeth P Cramer; Lars C Jacobsen; Maria T Larsen; Jack B Cowland; Julia T Tanassi; Niels H H Heegaard; Jonathan D Wren; Asli N Silahtaroglu; Niels Borregaard Journal: J Leukoc Biol Date: 2011-12-20 Impact factor: 4.962
Authors: Rheal A Towner; Randy L Jensen; Howard Colman; Brian Vaillant; Nataliya Smith; Rebba Casteel; Debra Saunders; David L Gillespie; Robert Silasi-Mansat; Florea Lupu; Cory B Giles; Jonathan D Wren Journal: Neurosurgery Date: 2013-01 Impact factor: 4.654
Authors: Stefano Tarantini; Cory B Giles; Jonathan D Wren; Nicole M Ashpole; M Noa Valcarcel-Ares; Jeanne Y Wei; William E Sonntag; Zoltan Ungvari; Anna Csiszar Journal: Age (Dordr) Date: 2016-08-26
Authors: Miguel Vazquez; Pedro Carmona-Saez; Ruben Nogales-Cadenas; Monica Chagoyen; Francisco Tirado; Jose Maria Carazo; Alberto Pascual-Montano Journal: Nucleic Acids Res Date: 2009-05-20 Impact factor: 16.971