Protein Folding : Symbolic Re nement Competeswith Neural

The Chou-Fasman Algorithm and its associated theory are non-learning methods to predict the secondary structure for proteins from the string of amino acids forming the protein. However, the overall accuracy of the predictions is not high and the predictions for the important structures are particularly low. Therefore, a range of methods have been used to improve the prediction of the secondary structure. We have applied our symbolic knowledge reenement system KRUST to the Chou-Fasman Theory to improve the accuracy of the predictions it makes. We compare our results with several other approaches: neural network, probabilistic and case-based. Symbolic reenement is particularly suitable since it makes use of an existing, but not very eeective, predictive theory and the learned result takes the form of a similar theory, with the same representation. Testing has revealed that our symbolic reenement approach yields competitive predictions to the other methods, together with the gains of human comprehensibility.