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Current methods, though useful, perform at a level well below what is possible, largely because performance of the latter deteriorates rapidly as coverage increases.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We introduce, test and apply a new decision rule, correlation enrichment (CE), for assigning genes to functional categories at various levels of resolution. Among the results are: (1) CE performs better than standard guilt by association (SGA, assignment to a functional category when a simple correlate exceeds a pre-specified threshold) irrespective of the number of genes assigned (<jats:italic>i.e<\/jats:italic>.<jats:italic>coverage<\/jats:italic>); improvement is greatest at high coverage where precision (positive predictive value) of CE is approximately 6-fold higher than that of SGA. (2) CE is estimated to allocate each of the 2918 unannotated orthologs to KEGG pathways with an average precision of 49% (approximately 7-fold higher than SGA) (3) An estimated 94% of the 1846 unannotated orthologs in the COG ontology can be assigned a function with an average precision of 0.4 or greater. (4) Dozens of functional and evolutionarily conserved cliques or quasi-cliques can be identified, many having previously unannotated genes.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The method serves as a general computational tool for annotating large numbers of unknown genes, uncovering evolutionary and functional modules. 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