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

Richard Maclin, Jude W. Shavlik

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings Tenth National Conference on Artificial Intelligence
PublisherPubl by AAAI
Pages165-170
Number of pages6
ISBN (Print)0262510634
StatePublished - Dec 1 1992
EventProceedings Tenth National Conference on Artificial Intelligence - AAAI-92 - San Jose, CA, USA
Duration: Jul 12 1992Jul 16 1992

Other

OtherProceedings Tenth National Conference on Artificial Intelligence - AAAI-92
CitySan Jose, CA, USA
Period7/12/927/16/92

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    Maclin, R., & Shavlik, J. W. (1992). Using knowledge-based neural networks to improve algorithms: refining the Chou-Fasman algorithm for protein folding. In Proceedings Tenth National Conference on Artificial Intelligence (pp. 165-170). Publ by AAAI.