Gene regulatory network from microarray data of colon cancer patients using TSK-type recurrent neural fuzzy network

In this work we applied a TSK-type recurrent neural fuzzy approach to extract regulatory relationship among genes and reconstruct gene regulatory network from microarray data. The identified signature has captured the regulatory relationship among 27 differentially expressed genes from microarray dataset. We applied three different methods viz., feed forward neural fuzzy, modified genetic algorithm and recurrent neural fuzzy, on the same data set for the inference of GRNs and the results obtained are almost comparable. In all tested cases, TRNFN identified more biologically meaningful relations. We found that 87.8% of the total interactions extracted by TRNFN are correct in accordance with the biological knowledge. Our analysis resulted in 2 major outcomes. First, upregulated genes are regulated by more genes than downregulated genes. Second, tumor activators activate other tumor activators and suppress tumor suppressers strongly in the disease environment. These findings will help to elucidate the common molecular mechanism of colon cancer, and provide new insights into cancer diagnostics, prognostics and therapy.

► The objective of the work is to extract gene regulatory network from microarray data of colon cancer patients.
► TSK-type recurrent neural fuzzy (TRNFN) approach has been used to extract regulatory relationship among genes.
► Three different algorithms were used on the data set to validate the result.
► TRNFN identified more biologically meaningful relations.
► The analysis resulted in two major outcomes.
► Upregulated genes are regulated by more genes than downregulated genes.
► Tumor activators activate other tumor activators and suppress tumor suppressers strongly in the disease environment.