Protein Secondary Structure Prediction Using Two-Level Case-Based Reasoning

B. Leng, B. G. Buchanan, and H. B. Nicholas

We have developed a two-level case-based reasonhag architecture for predicting protein secondary structure. The central idea is to break the problem into two levels: first, reasoning at the object (protein) level, and using the global information from this level to focus on a more restricted problem space; second, decomposing objects into pieces (segments), and reasoning at the internal structures level; finally, synthesizing the pieces back to the objects. The architecture has been implemented and tested on a commonly used data set with 69.3% predictive accuracy. It was then tested on a new data set with 67.3% accuracy. Additional experiments were conducted to determine the effects of using different similarity matrices.


This page is copyrighted by AAAI. All rights reserved. Your use of this site constitutes acceptance of all of AAAI's terms and conditions and privacy policy.