Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.eswa.2011.03.045 | Expert Syst. Appl. |
Keywords | Field | DocType |
sov99 accuracy,state overall per-residue accuracy,mixed-modal svm method,protein secondary structure prediction,secondary structure prediction,protein secondary structure,knowledge discovery,accurate protein,novel hybrid method,popular psipred method,mixed-modal bp method,predictive accuracy,compound pyramid model,predicting protein,direct tertiary structure modeling,amino acid,sequence analysis,domain knowledge | Data mining,Protein tertiary structure,Domain knowledge,Computer science,Threading (protein sequence),Threading (manufacturing),Support vector machine,Knowledge extraction,Pyramid,Artificial intelligence,Protein secondary structure,Machine learning | Journal |
Volume | Issue | ISSN |
38 | 9 | Expert Systems With Applications |
Citations | PageRank | References |
1 | 0.36 | 13 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bingru Yang | 1 | 186 | 26.67 |
| Wu Qu | 2 | 9 | 2.70 |
| Yonghong Xie | 3 | 122 | 14.43 |
| Yun Zhai | 4 | 1 | 0.36 |