Protein secondary structure prediction using data mining tool C5

Abstract

This paper reports our experimental results in protein secondary structure prediction using the machine learning software, C5. The accuracy improvement in the prediction of protein secondary structure is the focus of our study. Starting with a target protein with unknown secondary structures, we investigate three different approaches and find that training cases selected based on sequence homology can achieve the highest predictive accuracy of 75% in testing cases. Our result indicates that the method of selecting proteins for the training cases has the most significant impact on predictive accuracy.