A knowledge driven supervised learning approach to identify gene network of differentially up-regulated genes during neuronal senescence in Rattus norvegicus

Abstract

Various approaches have been described to infer the gene interaction network from expression data. Several models based on computational and mathematical methods are available. The fundamental thing in the identification of the gene interaction is their biological relevance. Two genes belonging to the same pathway are more likely to affect the expression of each other than the genes of two different pathways. In the present study, interaction network of genes is described based on upregulated genes during neuronal senescence in the Cerebellar granule neurons of rat. We have adopted a supervised learning method and used it in combination with biological pathway information of the genes to develop a gene interaction network. Further modular analysis of the network has been done to identify senescence-related marker genes. Currently there is no adequate information available about the genes implicated in neuronal senescence. Thus identifying multipath genes belonging to the pathway affected by senescence might be very useful in studying the senescence process.