Temporal Graphical Models for Cross-Species Gene Regulatory Network Discovery

Yan Liu*, Alexandru Niculescu-Mizil, Aurelie Lozano, Yong Lu

IBM T.J. Watson Research Center, Yorktown Heights, NY 10598, USA. liuya@us.ibm.com

Proc LSS Comput Syst Bioinform Conf. August, 2010. Vol. 9, p. 70-81. Full-Text PDF

*To whom correspondence should be addressed.

Many genes and biological processes function in similar ways across different species. Cross-species gene expression analysis, as a powerful tool to characterize the dynamical properties of the cell, has found a number of applications, such as identifying a conserved core set of cell cycle genes. However, to the best of our knowledge, there is limited effort on developing appropriate techniques to capture the causal relations between genes from time-series microarray data across species. In this paper, we present hidden Markov random field regression with L 1 penalty to jointly uncover the regulatory networks for multiple species. The algorithm provides a framework for sharing information across species via hidden component graphs and can conveniently incorporate domain knowledge over evolution relationship between species. We demonstrate the effectiveness of our method on two synthetic datasets and one innate immune response microarray dataset.