Microarray and other genome-wide technologies allow a global view of gene expression that can be used in several ways and whose potential has not been yet fully discovered. Functional insight into expression profiles is routinely obtained by using gene ontology terms associated to the cellular genes. In this article, we deal with functional data mining from expression profiles, proposing a novel approach that studies the correlations between genes and their relations to Gene Ontology (GO). We implemented this approach in a public web-based application named Fun&Co. By using Fun&Co, the user dissects in a pair-wise manner gene expression patterns and links correlated pairs to gene ontology terms. The proof of principle for our study was accomplished by dissecting molecular pathways in muscles. In particular, we identified specific cellular pathways by comparing the three different types of muscle in a pairwise fashion. In fact, we were interested in the specific molecular mechanisms regulating the cardiovascular system (cardiomyocytes and smooth muscle cells).