Phylogenetic profiles (PPs) are one of the most promising methods for predicting functional relationships by genomic context. The idea behind PPs is that if the products of two genes have a functional interdependence, the genes should both be either present or absent across genomes. One of the main problems with PPs is that evolutionarily close organisms tend to share a higher number of genes resulting in the overscoring of PP-relatedness. The proper measure of the overscoring effect of evolutionary redundancy requires examples of both functionally related genes (positive gold standards) and functionally unrelated genes (negative gold standards). Since experimentally verified functional interactions are only available for a few model organisms, there is a need for an alternative to gold standards. The presence of operons (polycistronic transcription units formed of functionally related genes) in prokaryotic genomes offers such an alternative. Genes in operons are located next to each other in the same DNA strand, and thus their presence should result in a higher proportion of predicted functional interactions among adjacent genes in the same strand than among adjacent genes in opposite strands. Under the preceding principle, we present a confidence value (CV) designed for evaluating predictions of functional interactions obtained using PPs. We first show that the CV corresponds to a positive predictive value calculated using experimentally known operons and further validate operon predictions based on this CV in other organisms using available microarray data. Then, we use a fixed CV of 0.90 as a reference to compare PP predictions obtained using different nonredundant genome datasets filtered at varying thresholds of genomic similarity. Our results demonstrate that nonredundant genome datasets increase the number of high-quality predictions by an average of 20%. Confidence values as those presented here should help compare other strategies and scoring systems to use phylogenetic profiles and other genomic context methods for predicting functional interactions.