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.