Protein Secondary Structure Predict!on Using Two-level Case-based Reasoning

We have developed a two-level case-based reason-hag architecture for predicting protein secondary struc-ture. 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, decom-posing 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 exper-iments were conducted to determine the effects of using different similarity matrices