Using knowledge-based neural networks to improve algorithms: Refining the Chou-Fasman algorithm for protein folding

摘要

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 thekbann 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 ofFSkbann 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 theFSkbann network.