Name
Title | ||
|---|---|---|
A method based on improved Bayesian inference network model and hidden Markov model for prediction of protein secondary structure |
Abstract | ||
|---|---|---|
This work aims at predicting the secondary structure of proteins, which is a complex nonlinear-mode classified problem. It proposes an algorithm which synchronises Bayesian network and hidden Markov model. It refers more neighbouring information of amino acid residue sequences for predicting secondary structure of the protein. Moreover it discusses data selection, network parameter determination and network performance in searching an algorithm of predicting protein secondary structure. The experimental results show feasibility and validity of the algorithm. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/CMPSAC.2004.1342695 | COMPSAC Workshops |
Keywords | Field | DocType |
protein secondary structure,bayesian inference network model,belief networks,network parameter determination,network performance,inference mechanisms,data selection,proteins,complex nonlinear-mode classified problem,biology computing,bayesian network,amino acid residue sequence,hidden markov model,improved bayesian inference network,secondary structure,hidden markov models | Variable-order Bayesian network,Maximum-entropy Markov model,Pattern recognition,Markov model,Computer science,Bayesian network,Artificial intelligence,Graphical model,Hidden Markov model,Causal Markov condition,Dynamic Bayesian network | Conference |
Volume | ISSN | ISBN |
2 | 0730-3157 | 0-7695-2209-2 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guohui Yang | 1 | 0 | 0.34 |
| Chunguang Zhou | 2 | 543 | 52.37 |
| Chengquan Hu | 3 | 13 | 7.05 |
| Zhezhou Yu | 4 | 22 | 5.50 |
| Hongji Yang | 5 | 1039 | 137.37 |