Expert Systems with Applications: An International Journal
Bayesian network multi-classifiers for protein secondary structure prediction
Artificial Intelligence in Medicine
Improving protein secondary structure prediction using a multi-modal BP method
Computers in Biology and Medicine
| Hi-index | 12.05 |
Accurate protein secondary structure predictions play an important role for direct tertiary structure modeling, and it also can significantly improve sequence analysis and sequence-structure threading for aiding in structure and function determination. Hence the improvement of predictive accuracy of the secondary structure prediction becomes essential for future development of the whole field of protein research. In this article, we propose a gradually enhanced, multi-layered prediction systematic model to predict protein secondary structure, Compound Pyramid Model (CPM). This model is composed of four independent coordination's layers by intelligent interfaces, synthesizes several methods, such as KDD^*, mixed-modal SVM method, mixed-modal BP method and so on. The model penetrates the whole domain knowledge, and the effective physicochemical properties of amino acids are imported. On the RS126 data set, state overall per-residue accuracy, Q"3, reached 83.99%, while segment overlap (SOV99) accuracy increased to 80.6%. On the CB513 data set, Q"3 reached 85.58%, SOV99 accuracy increased to 79.84%. Meanwhile, the results are found to be superior to those produced by other methods with blind test dataset CASP8's sequences, including the popular Psipred method according to Q"3 and SOV99 accuracy. The result shows that our method has strong generalization ability.