Refining Algorithms with Knowledge-Based Neural Networks: Improving the Chou-Fasman Algorithm for Protein Folding*

We describe a method for using machine learning to refine algorithms represented as generalized finite-state automata. The knowledge in an automaton is translated into a corresponding artificial neural network, and then refined by applying backpropagation to a set of examples. Our technique for translating an automaton into a network extends the KBANN algorithm, a system that translates a set of propositional, non-recursive rules into a corresponding neural network. The topology and weights of the neural network are set by KBANN so that the network represents the knowledge in the rules. We present the extended system, FSKBANN, which augments the KBANN algorithm to handle finite-state automata. We employ FSKBANN to refine the Chou-Fasman algorithm, a method for predicting how globular proteins fold. The Chou-Fasman algorithm cannot be elegantly formalized using non-recursive rules, but can be concisely described as a finite-state automaton. Empirical evidence shows that the refined algorithm FSKBANN produces is statistically significantly more accurate than both the original Chou-Fasman algorithm and a neural network trained using the standard approach. We also provide extensive statistics on the type of errors each of the three approaches makes and discuss the need for better definitions of solution quality for the protein-folding problem.