Gene regulatory network from microarray data using dynamic neural fuzzy approach

Abstract

The paper presents a multilayered dynamic neural fuzzy network (DNFN) to extract regulatory relationship among genes and reconstruct gene regulatory network for circulating plasma RNA data from colon cancer patients. This method combines the merits of connectionist and fuzzy approaches. It encodes the knowledge learned in the form of fuzzy rules and processes data following fuzzy reasoning principles. While the dynamic aspect of gene regulation was taken into account through the on-line learning of fuzzy rules, the structural learning together with the parameter learning form a fast learning algorithm for building a small, yet powerful, dynamic neural fuzzy network. One of the main advantages of DNFN is that there is no predetermination of hidden nodes, since it can find its optimal structure automatically and quickly. The inferred knowledge using the above network may provide biological insights that can be used to design and interpret further experiments.