A novel fuzzy logic based reverse engineering of gene regulatory network

Genes of an organism play a very crucial role in the working of various cellular activities. Genes and other biological molecules like DNA, RNA do not operate alone but they all are correlated. Their relationships are shown with the help of networks commonly known as Gene Regulatory Networks. Gene Regulatory Networks are complex control networks that show the map of interactions among the genes. They provide very useful contribution to the genomic science and increase the understanding of various biological processes. In this paper, fuzzy logic based method is proposed for the reverse engineering of gene regulatory network from microarray gene expression datasets. Pre-processing steps have been introduced to increase the efficiency of the method. Clustering technique is also employed to divide the problem into sub problems to reduce the computational complexity at some extent. Finally, the proposed method is tested on two different time course gene expression datasets of yeast having GEO accession number GDS37 and GDS3030. The results are validated by using Specificity, Sensitivity and F-score as parameters. Results of the proposed method are further compared with other existing method which was proposed by Al-Shobaili in 2014.