Inferring Gene Regulatory Networks using Heterogeneous Microarray Data Sets

Abstract

Inferring Gene Regulatory Networks (GRNs) is critical in describing the intrinsic relationship between genes in the course of evolution and discovering group behaviors of a certain set of genes. Recent development on high-throughput technique, microarray, provides researchers a chance to monitor the expression patterns of thousands of genes simultaneously. While increasing amount of microarray data sets are becoming available online, the integration of multiple microarray data sets from various data sources (e.g. different tissues, species, and conditions) for GRNs inference becomes very important in order to achieve more accurate and reliable GRNs modeling. This paper will review recent developments on integrating multiple microarray data sets and propose a new method to infer GRNs using multiple microarray data sets.