A new information-theoretic dissimilarity for clustering time-dependent gene expression profiles modeled with radial basis functions

The study and inference of biological pathways and gene regulation mechanisms has become a vital component of modern medicine and drug discovery. Gene expression studies make it possible to understand these mechanisms by simultaneously measuring the expression level of thousands of genes. These data though rich in information are also prone to many quality control issues that ultimately result in noisy data. A new method to smooth the data and measure expression dissimilarity between genes is proposed in this paper. A new dissimilarity measure is defined as an approximation of the Kullback-Leibler divergence between mixture models. Further, a noise reduction method is also proposed for use with data from time-course experiments. Results from real data and simulated data demonstrate that the method is well suited for clustering gene expression profiles.