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

Richard Maclin, Jude W. Shavlik

Research output: Contribution to journalArticlepeer-review

44 Scopus citations

Abstract

This article describes a connectionist method for refining algorithms represented as generalized finite-state 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 prepositional 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.

Original languageEnglish (US)
Pages (from-to)195-215
Number of pages21
JournalMachine Learning
Volume11
Issue number2
DOIs
StatePublished - May 1993

Keywords

  • Chou–Fasman algorithm
  • Multistrategy learning
  • finite-state automata
  • neural networks
  • protein folding
  • theory refinement

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