Name
Abstract | ||
|---|---|---|
Biological processes have produced the ultimate intelligent system, and now we are trying to understand biology by building intelligent systems. Protein Secondary structure prediction is essential for the tertiary structure modeling, and it is the one of the major challenge of bioinformatics. In this paper, we proposed a new type of intelligent system to predict the protein secondary structure, and it contain a Compound Pyramid Model (CPM) which is gradually enhanced, multi-layered. This model is composed of four independent coordination's layers by intelligent interfaces, synthesizes several methods. The model penetrates the whole domain knowledge, and the effective attributes are chosen by Causal Cellular Automata, and the high pure structure database is constructed for training. An optimized accuracy (Q3) for the RS126 and CB513 dataset of 83.99% and 85.58%, respectively, could be obtained. And the CASP8's sequences, the results are found to be superior to those produced by other methods, such as PSIPRED,SSPRO,SAM-T02,PHD Expert, PROF, JPRED, and so on. The result shows that our method has strong generalization ability. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/GrC.2010.68 | GrC |
Keywords | Field | DocType |
intelligent interface,cb513 dataset,protein secondary structure prediction,compound pyramid model,predicting protein secondary structure,causal cellular automata,tertiary structure modeling,protein secondary structure,high pure structure database,intelligent system,ultimate intelligent system,cellular automata,molecular biophysics,accuracy,biological process,classification algorithms,domain knowledge,intelligent systems,proteins,hidden markov models,prediction model,bioinformatics,data mining,predictive models | Cellular automaton,Data mining,Protein tertiary structure,Intelligent decision support system,Domain knowledge,Computer science,Pyramid,Artificial intelligence,Statistical classification,Hidden Markov model,Protein secondary structure,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 16 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bingru Yang | 1 | 186 | 26.67 |
| Lijun Wang | 2 | 15 | 3.46 |
| Wu Qu | 3 | 9 | 2.70 |
| Yun Zhai | 4 | 0 | 1.35 |