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

Authors:
Richard Maclin;Jude W. Shavlik
Affiliations:
Computer Sciences Department, University of Wisconsin, Madison, Wisconsin;Computer Sciences Department, University of Wisconsin, Madison, Wisconsin
Venue:
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Year:
1992

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

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Abstract

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 an artificial neural network, and then refined with backpropagation on a set of examples. Our technique for translating an automaton into a network extends KBANN, 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, we use FSKBANN to refine the Chou-Fasman algorithm, a method for predicting how globular proteins fold. Empirical evidence shows 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.