A model-based method for gene dependency measurement.

ABSTRACT: Many computational methods have been widely used to identify transcription regulatory interactions based on gene expression profiles. The selection of dependency measure is very important for successful regulatory network inference. In this paper, we develop a new method-DBoMM (Difference in BIC of Mixture Models)-for estimating dependency of gene by fitting the gene expression profiles into mixture Gaussian models. We show that DBoMM out-performs 4 other existing methods, including Kendall's tau correlation (TAU), Pearson Correlation (COR), Euclidean distance (EUC) and Mutual information (MI) using Escherichia coli, Saccharomyces cerevisiae, Drosophila melanogaster, Arabidopsis thaliana data and synthetic data. DBoMM can also identify condition-dependent regulatory interactions and is rob

SUBMITTER: Zhang Q 

PROVIDER: S-EPMC3400631 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature