Protein secondary structures prediction using data fusion approach

The importance of secondary protein structures is to help us to recognize many biological features, such as the structure and function of a protein, the evolutionary relation between proteins, and protein classification. Unfortunately, the secondary protein structures are hard to get from experimental analysis, and most researchers usually use predictive information instead of real structures. For protein secondary structure prediction, this research takes the predictive results from PSIPRED and PROF as the profile into the two-stage data fusion mechanism. The successive stage will integrate first stage outputs with our schemas. By performing the new approach, the accuracy of Q3 can be improved 5% more than the worst methods (PSIPRED or PROF) in the RS126 and CB513.