Reverse engineering of gene regulatory networks using flexible neural tree models

Abstract

The advances on DNA microarray technologies have enabled researchers to gain hundreds to thousands of gene expression levels. Much effect has been devoted over the past decade to analyze the gene expression data. In this study, flexible neural tree (FNT) model is used for gene regulatory network reconstruction and time-series prediction from gene expression profiling. We use voting strategy and Akaike information criterion (AIC) as two methods to identifying minimal regulatory elements of a target gene. A simulated dataset and three real biological datasets are used to test the validity of the FNT model. Results reveal that the FNT model can improve the prediction accuracy of microarray time-series data effectively and reconstruct gene regulatory network accurately.