Robust identification of transcriptional regulatory networks using a Gibbs sampler on outlier sum statistic.

ABSTRACT:

Motivation

Identification of transcriptional regulatory networks (TRNs) is of significant importance in computational biology for cancer research, providing a critical building block to unravel disease pathways. However, existing methods for TRN identification suffer from the inclusion of excessive 'noise' in microarray data and false-positives in binding data, especially when applied to human tumor-derived cell line studies. More robust methods that can counteract the imperfection of data sources are therefore needed for reliable identification of TRNs in this context.

Results

In this article, we propose to establish a link between the quality of one target gene to represent its regulator and the uncertainty of its expression to represent other target genes. Specifically, a

SUBMITTER: Gu J 

PROVIDER: S-EPMC3400952 | biostudies-literature | 2012 Aug

REPOSITORIES: biostudies-literature