Applied Bioinformatics

, Volume 4, Issue 4, pp 263–276

Random Walk Models for Bayesian Clustering of Gene Expression Profiles

Original Research Article

DOI: 10.2165/00822942-200504040-00006

Cite this article as:
Ferrazzi, F., Magni, P. & Bellazzi, R. Appl-Bioinformatics (2005) 4: 263. doi:10.2165/00822942-200504040-00006


The analysis of gene expression temporal profiles is a topic of increasing interest in functional genomics. Model-based clustering methods are particularly interesting because they are able to capture the dynamic nature of these data and to identify the optimal number of clusters. We have defined a new Bayesian method that allows us to cope with some important issues that remain unsolved in the currently available approaches: the presence of time dislocations in gene expression, the non-stationarity of the processes generating the data, and the presence of data collected on an irregular temporal grid. Our method, which is based on random walk models, requires only mild a priori assumptions about the nature of the processes generating the data and explicitly models inter-gene variability within each cluster. It has first been validated on simulated datasets and then employed for the analysis of a dataset relative to serum-stimulated fibroblasts. In all cases, the results have been promising, showing that the method can be helpful in functional genomics research.

Copyright information

© Adis Data Information BV 2005

Authors and Affiliations

  • Fulvia Ferrazzi
    • 1
  • Paolo Magni
    • 1
  • Riccardo Bellazzi
    • 1
  1. 1.Dipartimento di Informatica e SistemisticaUniversità di PaviaPaviaItaly