Protein fold prediction using cluster merging

Protein folding prediction, also called protein structure prediction, is one of the most important issues for understanding living organisms. Therefore, predicting the folding structure of proteins from their linear sequence is a very big challenge in biology. Despite years of research and the wide variety of approaches, protein folding still remains a difficult problem. One of the main difficulties is controlling the over-fitting and under-fitting behavior of classifiers in the prediction systems. In this paper we propose a new learning method to improve the accuracy of protein folding prediction by balancing between over-fitting and under-fitting. The key of this method is based on a special way for analyzing the distance among training data points in order to cluster them into spaces which have high density of data points. By this, the over fitting and under fitting can be controlled in a comprehensive manner. Some experimental results seem to indicate that the proposed method has a significant potential on improve the accuracy of protein folding prediction.