The interpretation of feedforward neural networks for secondary structure prediction using sugeno fuzzy rules

Abstract

This paper presents results from the rule extraction process applied on multilayer neural networks for the prediction of protein backbone dihedral angles based on the physical-chemical attributes of the amino acids. The goals are to analyze the knowledge acquired by the neural network in the form of a fuzzy inference system of Sugeno fuzzy rules, to explain the results obtained by the scientific community when processing the Hydropathy Index and the Isoeletric Point and to show that the rule extraction process is an important tool to analyze neural networks. The proposed extraction algorithm and the rules are shown and discussed in the context of the application, relating them to the original proteins from which the data was computed. It is shown that the proposed rules are simple in the sense of being linear in relation to the input parameters, and therefore accessible to anyone with basic scientific knowledge. We also present a validation of rules from a statistical point of view.