A random-periods model for the comparison of a metrics efficiency to classify cell-cycle expressed genes

This paper addresses the clustering and classification of active genes during the process of cell division. Cell division ensures the proliferation of cells, but it becomes increasingly abnormal in cancer cells. The genes studied here are described by their expression profiles (i.e. time series) during the cell division cycle. This work focuses on evaluating the efficiency of four major metrics for clustering and classifying genes expression profiles and is based on a random-periods model for the expression of cell-cycle genes. The model accounts for the observed attenuation in cycle amplitude or duration, variations in the initial amplitude, and drift in the expression profiles.