title: Predictive Integration of Gene Ontology-Driven Similarity and Functional Interactions.
abstract: There is a need to develop methods to automatically incorporate prior knowledge to support the prediction and validation of novel functional associations. One such important source is represented by the Gene Ontology (GO)™and the many model organism databases of gene products annotated to the GO. We investigated quantitative relationships between the GO-driven similarity of genes and their functional interactions by analyzing different types of associations in Saccharomyces cerevisiae and Caenorhabditis elegans. Interacting genes exhibited significantly higher levels of GO-driven similarity (GOS) in comparison to random pairs of genes used as a surrogate for negative interactions. The Biological Process hierarchy provides more reliable results for co-regulatory and protein-protein interactions. GOS represent a relevant resource to support prediction of functional networks in combination with other resources.
authors.affiliation: School of Computing and Mathematics, University of Ulster, UK.
authors.forename: Francisco
authors.initials: F
authors.lastname: Azuaje
authors.affiliation: School of Computing and Mathematics, University of Ulster, UK.
authors.forename: Haiying
authors.initials: H
authors.lastname: Wang
authors.affiliation: School of Computing and Mathematics, University of Ulster, UK.
authors.forename: Huiru
authors.initials: H
authors.lastname: Zheng
authors.affiliation: National Library of Medicine, National Institutes of Health., USA.
authors.forename: Olivier
authors.initials: O
authors.lastname: Bodenreider
authors.affiliation: High-Throughput Protein Technologies Group, EMBL-Grenoble, France.
authors.forename: Alban
authors.initials: A
authors.lastname: Chesneau