Using Knowledge-Based Neural Networks to Improve Algorithms: Refining the Chou-Fasman Algorithm for Protein Folding

Abstract

This article describes a connectionist method for refining algorithms represented as generalized finitestate automata. The method translates the rule-like knowledge in an automaton into a corresponding artificial neural network, and then refines the reformulated automaton by applying backpropagation to a set of examples. This technique for translating an automaton into a network extends the kbann algorithm, a system that translates a set of propositional rules into a corresponding neural network. The extended system, FSkbann , allows one to refine the large class of algorithms that can be represented as state-based processes. As a test, FSkbann is used to improve the Chou-Fasman algorithm, a method for predicting how globular proteins fold. Empirical evidence shows that the multistrategy approach of FSkbann leads to a statistically-significantly, more accurate solution than both the original Chou-Fasman algorithm and a neural network trained using the standard approach. Extensive statistics report the types of errors made by the Chou-Fasman algorithm, the standard neural network, and the FSkbann network.