Most investigations of coordinated gene expression have focused on identifying correlated expression patterns between genes by examining their normalized static expression levels. In this study, we focus on the dynamics of gene expression by seeking to identify correlated patterns of changes in genetic expression level. In doing so, we build upon methods developed in clinical informatics to detect temporal trends of laboratory and other clinical data. We construct relevance networks from Saccharomyces cerevisiae gene-expression dynamics data and find genes with related functional annotations grouped together. While some of these associations are also found using a standard expression level analysis, many are identified exclusively through the dynamic analysis. These results strongly suggest that the analysis of gene expression dynamics is a necessary and important tool for studying regulatory and other functional relationships among genes. The source code developed for this investigation is freely available to all non-commercial investigators by contacting the authors.