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

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. Here we applied data mining techniques on genomic, literature and signaling databases to obtain the required information for pathway inference. An R script was developed in R that discovers pathways using edge information in different signaling databases. We explain how to distinguish more valid pathways from the invalid ones using molecular information in the papers and genomic data analysis. We performed this pathway discovery approach on proto-oncogene c-Src to identify new pathways containing Src and genes that their expression were affected by Src overactivation. This integrative method uses three sources of information which help bioinformaticians who work with gene regulatory network when they want to infer causal relationships between components of a biological system. In addition, some potential positive and negative feedbacks along with predicted pathways were proposed based on the gene expression results. In fact, this flowchart will open new insights into the interactions between cellular components and help biologists look for new possible molecular relationships that have not been reported neither in signaling databases nor as a pathway.