Abstract-The Gene Ontology and annotations derived from the S. cerivisirre Genome 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 o i 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.
Similarly, Wang REF investigated the correlation between gene expression and similarity based on information extracted from the GO and the aspects composing it.
Gene expression correlation and gene ontology-based similarity: an assessment of quantitative relationships
2004 Symposium on Computational Intelligence in Bioinformatics and Computational Biology
2004 Symposium on Computational Intelligence in Bioinformatics and Computational Biology
Medicine Biology Computer Science