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BackgroundBiologists may need to know the set of genes that are semantically related to a given set of genes. For instance, a biologist may need to know the set of genes related to another set of genes known to be involved in a specific disease. Some works use the concept of gene clustering in order to identify semantically related genes. Others propose tools that return the set of genes that are semantically related to a given set of genes. Most of these gene similarity measures determine the semantic similarities among the genes based solely on the proximity to each other of the GO terms annotating the genes, while overlook the structural dependencies among these GO terms, which may lead to low recall and precision of results.ResultsWe propose in this paper a search engine called GRank, which overcomes the limitations of the current gene similarity measures outlined above as follows. It employs the concept of existence dependency to determine the structural dependencies among the GO terms annotating a given set of gene. After determining the set of genes that are semantically related to input genes, GRank would use microarray experiment to rank these genes based on their degree of relativity to the input genes. We evaluated GRank experimentally and compared it with a comparable gene prediction tool called DynGO, which retrieves the genes and gene products that are relatives of input genes. Results showed marked improvement.ConclusionsThe experimental results demonstrated that GRank overcomes the limitations of current gene similarity measures. We attribute this performance to GRank’s use of existence dependency concept for determining the semantic relationships among gene annotations. The recall and precision values for two benchmarking datasets showed that GRank outperforms DynGO tool, which does not employ the concept of existence dependency. The demo of GRank using 11000 KEGG yeast genes and a Gene Expression Omnibus (GEO) microarray file named “GSM34635.pad” is available at: http://ecesrvr.kustar.ac.ae:8080/ (click on the link labelled Gene Ontology 2).