| Title | A dynamic Bayesian network approach to protein secondary structure prediction |
| Authors | Yao, Xin-Qiu Zhu, Huaiqiu She, Zhen-Su |
| Affiliation | Peking Univ, State Key Lab Turbulence & Complex Syst, Beijing 100871, Peoples R China. Peking Univ, Dept Biomed Engn, Beijing 100871, Peoples R China. Peking Univ, Ctr Theoret Biol, Beijing 100871, Peoples R China. Univ Calif Los Angeles, Dept Math, Los Angeles, CA 90095 USA. |
| Keywords | SEQUENCE ALIGNMENT PROFILES SUPPORT VECTOR MACHINES RECOGNITION INFORMATION ACCURACY DATABASE MODELS |
| Issue Date | 2008 |
| Publisher | bmc bioinformatics |
| Citation | BMC BIOINFORMATICS.2008,9. |
| Abstract | Background: Protein secondary structure prediction method based on probabilistic models such as hidden Markov model (HMM) appeals to many because it provides meaningful information relevant to sequence-structure relationship. However, at present, the prediction accuracy of pure HMM-type methods is much lower than that of machine learning-based methods such as neural networks (NN) or support vector machines (SVM). Results: In this paper, we report a new method of probabilistic nature for protein secondary structure prediction, based on dynamic Bayesian networks (DBN). The new method models the PSI-BLAST profile of a protein sequence using a multivariate Gaussian distribution, and simultaneously takes into account the dependency between the profile and secondary structure and the dependency between profiles of neighboring residues. In addition, a segment length distribution is introduced for each secondary structure state. Tests show that the DBN method has made a significant improvement in the accuracy compared to other pure HMM-type methods. Further improvement is achieved by combining the DBN with an NN, a method called DBNN, which shows better Q(3) accuracy than many popular methods and is competitive to the current state-of-the-arts. The most interesting feature of DBN/DBNN is that a significant improvement in the prediction accuracy is achieved when combined with other methods by a simple consensus. Conclusion: The DBN method using a Gaussian distribution for the PSI-BLAST profile and a high-ordered dependency between profiles of neighboring residues produces significantly better prediction accuracy than other HMM-type probabilistic methods. Owing to their different nature, the DBN and NN combine to form a more accurate method DBNN. Future improvement may be achieved by combining DBNN with a method of SVM type. |
| URI | http://hdl.handle.net/20.500.11897/154062 |
| ISSN | 1471-2105 |
| DOI | 10.1186/1471-2105-9-49 |
| Indexed | SCI(E) PubMed |
| Appears in Collections: | 湍流与复杂系统国家重点实验室 工学院 |
