A recurrent fuzzy neural model of a gene regulatory network for knowledge extraction using invasive weed and artificial bee colony optimization algorithm

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 IWO-ABC-based search algorithm to unveil potential genetic network constructions that fit well with the time-series data and explore possible gene interactions.