Back
 JBiSE  Vol.9 No.10 B , September 2016
A Prediction Method of Protein Disulfide Bond Based on Hybrid Strategy
Abstract:
A prediction method of protein disulfide bond based on support vector machine and sample selection is proposed in this paper. First, the protein sequences selected are en-coded according to a certain encoding, input data for the prediction model of protein disulfide bond is generated; Then sample selection technique is used to select a portion of input data as training samples of support vector machine; finally the prediction model training samples trained is used to predict protein disulfide bond. The result of simulation experiment shows that the prediction model based on support vector ma-chine and sample selection can increase the prediction accuracy of protein disulfide bond.
Cite this paper: Sun, P. , Ding, Y. , Huang, Y. and Zhang, L. (2016) A Prediction Method of Protein Disulfide Bond Based on Hybrid Strategy. Journal of Biomedical Science and Engineering, 9, 116-121. doi: 10.4236/jbise.2016.910B015.
References

[1]   Finalski, K. (2006) Comparative Modeling for Protein Structure Prediction. Current Opinion in Structural Biology, 16, 1-6.

[2]   Savojardo, C., Fariselli, P., Alhamdoosh, M. and Martelli, P.L. (2011) A Pierleoni: Improving the Prediction of Disulfide Bonds in Eukaryotes with Machine Learning Method and Protein Subcellular Localization. Bioinformatics, 27, 2224-2230. http://dx.doi.org/10.1093/bioinformatics/btr387

[3]   Yang, J., He, B.-J., Jang, R., Zhang, Y. and Shen, H.-B. (2015) A Ccurate Disul-fide-Bonding Network Predictions Improve ab into Structure Prediction of Cysteine-Rich Protein. Bioinformatics, 31, 3773-3781.

[4]   Tessier, D., Bardiaux, B., Larre, C. and Popineau, Y. (2004) Data Mining Techniques to Study the Disulfide-Bonding State in Proteins: Signal Peptide Is a Strong Descriptor. Bioinformatics, 20, 2509-2512. http://dx.doi.org/10.1093/bioinformatics/bth332

[5]   Vullo, A. and Passerini, A. (2004) Disulfide Connectivity Prediction Using Recursive Neural Networks and Evolutionary Information. Bioinformatics, 20, 653-659. http://dx.doi.org/10.1093/bioinformatics/btg463

[6]   Mucchielli-Giorgi, M.H., Hazout, S. and Tuffery, P. (2002) Predicting the Disulfide Bonding State of Cysteines Using Protein Descriptors. Proteins, 46, 243-249. http://dx.doi.org/10.1093/bioinformatics/btg463

[7]   Chapelle, O., Vapnik, V., Bacsquest, O., et al. (2002) Choosing Multiple Parameters for Support Vector Machines. Machine Learning, 46, 131-159. http://dx.doi.org/10.1023/A:1012450327387

[8]   Lee, K., Chung, Y. and Byun, H. (2002) SVM-Based Face Verification with Feature Set of Small Size. Electronics Letters, 38, 787-789. http://dx.doi.org/10.1049/el:20020591

[9]   Hao, H.-W. and Jiang, R.-R. (2007) Training Sample Selection Method for Neural Networks Based on Nearest Neighbor Rule. Acta Automatica Sinica, 33, 1247-1251.

[10]   Sun, P.F., Cui, Y.Q., Chen, T.K. and Zhao, Y. (2013) Prediction Method of Protein Disulfide Bond Based on Pattern Selection. Engineering, 5, 409-412. http://dx.doi.org/10.4236/eng.2013.510B083

[11]   Pirovano, W. and Heringa, J. (2010) Protein Secondary Structure Prediction. Methods Mol Biol, 609, 327-348. http://dx.doi.org/10.1007/978-1-60327-241-4_19

[12]   Dodge, C., Schneider, R., Sander, C. (1998) The HSSP Database of Protein Structure-Se- quence Alignments and Family Profiles. Nucleic Acids Res., 26, 313-315. http://dx.doi.org/10.1093/nar/26.1.313

 
 
Top