Pathway mining in functional genomics: An integrative approach to delineate boolean relationships between Src and its targets

Abstract

In recent years the volume of biological data has soared. Parallel to this growth, the need for developing data mining strategies has not met sufficiently. Bioinformaticians utilize different data mining techniques to obtain the required information they need from genomic, transcriptomic and proteomic databases to construct a gene regulatory network (GNR). One of the simplest mining approaches to construct a GNR is reading a great number of papers to configure a large network (for instance, a network with 50 nodes and 200 edges) which takes a lot of time and energy. Here we introduce an integrative method that combines information from transcriptomic data with sets of constructed pathways. A program was written in R that makes pathways from edgelists in different signaling databases. Furthermore, we explain how to distinguish false pathways from the correct ones using literature study or incorporating the biological information with gene expression results.This approach can help bioinformaticians and mathematical biologists who work with GRNs when they want to infer causal relationships between components of a biological system. Once you know which direction to go, you will reduce the number of false results.