Inferring Fuzzy Cognitive Map models for Gene Regulatory Networks from gene expression data

Gene Regulatory Networks (GRNs) represent the causal relations among the genes and provide insight on the cellular functions and the mechanism of the diseases. GRNs can be inferred from gene expression data by a number of algorithms, e.g. Boolean networks, Bayesian networks, and differential equations. While reliable inference of GRNs is still an open problem, new algorithms need to be developed. Fuzzy Cognitive Maps (FCMs) is used to represent GRNs in this paper. Most of the FCM learning algorithms are able to learn FCMs with less than 40 nodes. A new algorithm that is able to learn FCMs with more than 100 nodes is proposed. The proposed method is based on Ant Colony Optimization (ACO). A decomposed approach is proposed to reduce the dimension of the problem; therefore the FCM learning algorithm is more scalable (the dimension of the problem to be solved in one ACO run equals to the number of nodes or genes). The proposed approach is tested on data from DREAM project. The experiment results suggest the proposed approach outperforms several other algorithms.