Ranking Novel Regulatory Genes in Gene Expression Profiles using NetExpress

Understanding gene regulation by identifying gene products and determining their roles in regulatory networks is a complex process. A common computational method is to reverse engineer a regulatory network from gene expression profile, and sanitize the network using known information about the genes, their interactions and other properties to filter out unlikely interactors. Unfortunately, due to limited resources most gene expression studies have a limited and small number of time points, and most reverse engineering tools are unable to handle large numbers of genes. Both of these factors play significant roles in influencing the accuracy of the process. In this paper, we present a new gene ranking algorithm from gene expression profiles with a small number of time points so that the most relevant genes can be selected for reverse engineering. We also present a graphical interface called NetExpress, which adopts this algorithm and allows users to set control parameters to effect the desired outcome, and visualize the analysis for iterative fine tuning.