Clustering gene expression by dynamics: A maximum entropy approach

Arrays allow simultaneous measurements of the expression levels of thousands of mRNAs. By mining this data one can identify sets of genes with similar profiles. We show that information theoretic methods are capable of modeling and assessing dissimilarities between the dynamics underlying to the gene expression time series. By recourse of a maximum entropy-based method for building models, we built a distance between two gene expression profiles, which takes into account the dynamic features of the expression. The proposed distance measure can be implemented over a wide variety of clustering algorithms enhancing their usefulness.