Inferring functional gene relationships is a major step in understanding biological networks. With microarray data from an increasing number of biologically interrelated experiments, it now allows for more complete portrayals of functional gene relationships involved in biological processes. In current studies of gene relationships, the existence of dependencies between gene expressions from the biologically interrelated experiments, however, has been widely ignored. When not accounted for, these experimental dependencies can result in inaccurate inferences of functional gene relationships, and hence incorrect biological conclusions. This article proposes a statistical framework and a novel gene co?expression measure, named Knorm correlation, to address this problem. The most important aspect of the proposed model is its ability to decompose the interesting biological variations in gene expressions into two mutually independent components each arising from the genes and the experiments, in addition to variations due to random noises. As a result, the Knorm correlation can critically de-correlate the experimental dependencies before estimating the gene relationships, thus leading to improved accuracies in inferring functional gene relationships. Knorm correlation simplifies to the Pearson coefficient when experiments are uncorrelated. Using simulation studies, a yeast microarray and a human microarray dataset, we demonstrate the success of the Knorm correlation as a more accurate and reliable measure, and the adverse impact of experimental dependencies on the Pearson coefficient, in inferring functional gene relationships from interrelated and interdependent experiments