Learning of Fuzzy Cognitive Maps for modelling Gene Regulatory Networks through Big Bang-Big Crunch algorithm

Inferring Gene Regulatory Networks (GRNs) from expression data is one of the most challenging topic in computational biology. Indeed, the reasoning about GRN behaviours is a crucial biological task useful to provide an significant support for the identification of genetic diseases and the estimation of the effects of medications. Over years, several approaches have been applied to infer GRNs, most of them are based on deterministic and crisp-based algorithms. However, the intrinsic imprecise nature of the gene regulation makes these approaches as inefficient and characterized by a low accuracy. Starting from this consideration, in this work, we propose to use Fuzzy Cognitive Maps to model the complex behaviour of GRNs and to learn FCMs models of GRNs by means of an innovative evolutionary algorithm: the Big Bang-Big Crunch algorithm. As shown through a statistical comparison, the proposed approach outperforms other evolutionary learning methods in inferring GRNs representing, as a consequence, a breakthrough approach in this fascinating and challenging domain.