Inferring gene interactions from microarray gene expression data using fuzzy Petri net

This paper describes the problem of inferring the complex causal relationships among genes from microarray experimental data based on a fuzzy Petri net (FPN). The method derives information on the gene interactions in a highly interpretable form (fuzzy rules) and takes into account dynamical aspects of genes regulation. The approach of the fuzzification of the Petri net is proposed. A token in the FPN is described as a membership function of a linguistic term. A transition, specified as a production rule, can be fired if the conditions satisfied. Additionally, a fuzzy Petri net is used with a recurrent neuro-fuzzy network for the modeling. A case study is used to illustrate the approach. For evaluation, the proposed technique has been tested using real expression data and experimental results show that the use of fuzzy Petri net based technique in gene expression data analysis can be quite effective.