GRank: a middleware search engine for ranking genes by relevance to given genes.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:23957362", "kind": "science-record", "revision": 4, "content_hash": "sha256:eb7abbf360718f3f3ff7e975b01e117df710bd574a32eb8a0a4d09294a42781c", "created_at": "2026-11-01T15:40:03Z", "updated_at": "2026-11-01T21:19:43Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "GRank: a middleware search engine for ranking genes by relevance to given genes.", "doi": "10.1186/1471-2105-14-251", "pmid": "23957362", "authors": ["Kamal Taha", "Dirar Homouz", "Hassan Al Muhairi", "Zaid Al Mahmoud"], "journal": "BMC bioinformatics", "year": 2013, "abstract": "BACKGROUND: Biologists 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.\nRESULTS: We 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.\nCONCLUSIONS: The 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).", "claims": [{"id": "claim-method", "statement": "GRank finds the meaningful Lowest Common Ancestors (LCA) and the meaningful Top Common Descendants (TCD) of the set ST. A meaningful LCA is a LCA in GO graph, on which the existence of ST depends. A meaningful TCD is a TCD in GO graph, whose existence depends on the set ST. GRank would then rank the meaningful LCAs and TCDs, which are then converted back to genes based on annotations and retrieved back to the user.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:23957362", "locator": "sections[2] \"Methods\" paragraph 4", "quote": "GRank finds the meaningful Lowest Common Ancestors (LCA) and the meaningful Top Common Descendants (TCD) of the set ST. A meaningful LCA is a LCA in GO graph, on which the existence of ST depends. A meaningful TCD is a TCD in GO graph, whose existence depends on the set ST. GRank would then rank the meaningful LCAs and TCDs, which are then converted back to genes based on annotations and retrieved back to the user."}], "state": "complete"}, {"id": "claim-data-access", "statement": "We used for the evaluation two different benchmarks: KEGG and Pfam benchmarks. We selected a set of 15 human and 15 yeast diverse KEGG pathways (see Tables 2 and 3); the genes were retrieved using the DBGET database [22]. We selected 15 groups of highly related Pfam entries (see Table 4) from the Sanger Pfam database [23].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:23957362", "locator": "sections[11] \"Benchmarking datasets\" paragraph 3", "quote": "We used for the evaluation two different benchmarks: KEGG and Pfam benchmarks. We selected a set of 15 human and 15 yeast diverse KEGG pathways (see Tables 2 and 3); the genes were retrieved using the DBGET database [22]. We selected 15 groups of highly related Pfam entries (see Table 4) from the Sanger Pfam database [23]."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": null, "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:23957362"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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