Functional identification in correlation networks using gene ontology edge annotation.

Correlation networks identify mechanisms behind observed change in temporal data sets; however, it is often difficult to discriminate between causative versus coincidental structures in such networks. We propose a method to enhance causative relationships based on annotations derived from the Gene Ontology (GO). Enriching correlation networks with biological relationships is likely to conserve relevant signals while reducing the network size. The obtained results are structures enriched in GO functions, despite reduction in network size. Our proposed method annotates edges according to the shortest path between elements and the position of the deepest common parent in the GO tree. Our results show that such enrichment brings functional relationships to the forefront which allows for the identification of clusters with significant biological relevance. Further, this method impacts the identification of essential genes within a network model. This approach for uncovering true function of relationships provides annotation beyond traditional statistical analysis.