A recurrent fuzzy neural model of a gene regulatory network for knowledge extraction using Artificial Bee Colony optimization algorithm

Abstract

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 connection weight matrix. Based on the fact that the measured time points are limited and the assumption that the genetic networks are usually sparsely connected, we present an ABC-based search algorithm to unveil potential genetic network constructions that fit well with the time-series data and explore possible gene interactions. A cost function is designed, the minimization of which yields the solution to the problem. Computer simulation of the proposed algorithm reveals that it is able to predict the signs of all the existing weights accurately.