title: Gene Expression Correlation and Gene Ontology-Based Similarity: An Assessment of Quantitative Relationships.
abstract: The Gene Ontology and annotations derived from theS. cerivisiaeGenome Database were analyzed to calculate functional similarity of gene products. Three methods for measuring similarity (including a distance-based approach) were implemented. Significant, quantitative relationships between similarity and expression correlation of pairs of genes were detected. Using a known gene expression dataset in yeast, this study compared more than three million pairs of gene products on the basis of these functional properties. Highly correlated genes exhibit strong similarity based on information originating from the gene ontology taxonomies. Such a similarity is significantly stronger than that observed between weakly correlated genes. This study supports the feasibility of applying gene ontology-driven similarity methods to functional prediction tasks, such as the validation of gene expression analyses and the identification of false positives in protein interaction studies.
authors: School of Computing and Mathematics, University of Ulster, BT37 0QB, UK.
authors: Haiying
authors: H
authors: Wang
authors: School of Computing and Mathematics, University of Ulster, BT37 0QB, UK.
authors: Francisco
authors: F
authors: Azuaje
authors: U.S. National Library of Medicine, National Institutes of Health, Department of Health & Human Services, 8600 Rockville Pike, Bethesda, Maryland 20894, U.S.A.
authors: Olivier
authors: O
authors: Bodenreider
authors: Bioinformatics Unit, Spanish National Cancer Centre (CNIO), Melchor Fernandez Almagro 3, 28039, Madrid, Spain.
authors: Joaquín
authors: J
authors: Dopazo
keywords: Gene Ontology; functional similarity; gene expression correlation