Transcriptional Gene Regulatory Network Reconstruction Through Cross Platform Gene Network Fusion

Abstract

Abstract. Microarray gene expression data is used to model differential activity in Gene Regulatory Networks (GRN) to elucidate complex cellular processes, though network modeling is susceptible to errors due to both noisy nature of gene expression data and platform bias. This intuitively provided the motivation for the development of an innovative technique, which effectively integrates GRN using cross-platform data to minimize the two aforementioned effects. This paper presents a GRN integration (GeNi) framework that fuses crossplatform GRN to remove platform and experimental bias using the Dempster Shafer Theory of Evidence. The proposed model estimates gene co-regulation strength by using mutual information and removes spurious co-regulations through data processing inequality. The method automatically adapts to the data distribution using Belief theory, which does not require a preset threshold to accept co-regulated links.1 GeNi is applied to identify common cancer-related regulatory links in ten different datasets generated by different microarray platforms including cDNA and Affymetrix arrays. Experimental results demonstrate that GeNi can be effectively applied for GRN reconstruction and cross-platform gene network fusion for any gene expression data.

Information theoretical methods for complex network structure reconstruction

“…For instance, if node (or agent) A has a high value of conditional correlation (say, mutual information) with node B, and B is also highly correlated with node C, most common algorithms would predict (with a marginal probability p ind ) the presence of a (possibly non-existent) link between processes A and C. In order to correct for the presence of indirect links we may implement some methods from IT, such as bounds in the information-theoretical probability measures and the use of the Data Processing Inequality (DPI) (Sehgal et al 2007). DPI can provide a bound to the extent on which signal processing may optimize probabilistic inference.…”