Fuzzy Relational System for Identification of Gene Regulatory Network

Generating inferences from a gene regulatory network is important to understand the fundamental cellular processes, involving gene functions, and their relations. The availability of time-series gene expression data makes it possible to investigate the gene activities of the whole genomes. Under this framework, gene interaction is explained through a set of fuzzy relational matrices. By transforming quantitative expression values into linguistic terms, the proposed technique defines a measure of fuzzy dependency among genes. Based on the fact that the measured time points are limited, we present an Artificial Bee Colony-based search algorithm to unveil potential genetic network constructions that fit well with the time-series data and explore possible gene interactions. Keywordsgene regulatory network; fuzzy relational system; fuzzy membership distribution; artificial bee colony optimization algorithm; differential evolution algorithm.