Commenced in January 2007
Paper Count: 31103
Predicting Protein Interaction Sites Based on a New Integrated Radial Basis Functional Neural Network
Abstract:Interactions among proteins are the basis of various life events. So, it is important to recognize and research protein interaction sites. A control set that contains 149 protein molecules were used here. Then 10 features were extracted and 4 sample sets that contained 9 sliding windows were made according to features. These 4 sample sets were calculated by Radial Basis Functional neutral networks which were optimized by Particle Swarm Optimization respectively. Then 4 groups of results were obtained. Finally, these 4 groups of results were integrated by decision fusion (DF) and Genetic Algorithm based Selected Ensemble (GASEN). A better accuracy was got by DF and GASEN. So, the integrated methods were proved to be effective.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1063304Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1181
 W. Xiangyu, C. Shouliang and G. Mingde, General Biology,2rd ed. Beijing: Higher Education Press, 2005.
 G. Drewes and T. Bouwmeester, Global approaches to protein-protein interactions. Curr.Opin.Cell.Biol. 15, 1-7, 2003.
 Y. Changhui, H. Vasant and D. Drena, Predicting Protein-Protein Interaction Sites From Amino Acid Sequence. Technical report, Iowa State University, 2002.
 F. S'ebastien and Z. Martin, Prediction of Protein-Protein Interaction Sites Using Electrostatic Desolvation Profiles. Biophys. J. 98, 1921-1930, 2010.
 L. F. Consuelo, M. M. dos S.Roberta, J. M. Angelo, R. de M.F. Marcos, C. Carlos and G. Claude, Identification of continuous interaction sites in PLA2-based protein complexes by peptide arrays. Biochimie. 91, 1482- 1492, 2009.
 S. Mile, T. Sanja and V. Kristian, Prediction of Protein-Protein Interaction Sites in Sequences and 3D Structures by Random Forests. PloS. Comput. Biol. 5, 1-9, 2009.
 L. Man, L. T. Chew and S. Jian, Feature generation and representations for protein-protein interaction classification. J. Biomed. Inform. 42, 866- 872, 2009.
 P. B. John and R. B. Lauren, Sensitivity of RBF interpolation on an otherwise uniform grid with a point omitted or slightly shifted. Appl. Numer. Math. 60, 659-672, 2010.
 F. A.H. Mohamed, S. Friedhelm, P. G┬¿unther, Semi-supervised learning for tree-structured ensembles of RBF networks with Co-Training. Neur. Netw. 23, 497-509, 2010.
 M. M. Gonzalo and S. Alberto, Out-of-bag estimation of the optimal sample size in bagging. Pattern. Recognit. 43, 143-152, 2010.
 S. Noritaka, M. Hiromi, M. Michiharu and M. Lixin, Bagging and AdaBoost algorithms for vector quantization. Neurocomputing. 73, 106- 114, 2009.
 C. Guo and Z. Hongfu, Fusion Diagnosis for EngineWear Fault Based on Integrated Neural Network. J. Nanjing Univ. Aero & Astr. 36, 278-283, 2004.
 Z. Zhihua, W. Jianxin and T. Wei, Ensembling neural networks: Many could be better than all. Artif. Intell. 137, 239-263, 2002.
 E. Iakes, B. Lisa, F. Piero, C. Rita, V. Alfonso and L. T. Michael, Progress and challenges in predicting protein-protein interaction sites. Brief. Bioinform. 10, 233-246, 2009.
 L. Yang, T. Zhengquan and W. Yifei, SVM-based protein interaction sites prediction. Technical report, Shanghai University, 2006.
 L. Chun and Q. Weiyi, Mathematical description of biological macromolecules and its application. Liaoning: Dalian University of Technology Press, 2009.
 Comparison of SURFACE and AREAIMOL for accessible surface area calculations, http://www.ccp4.ac.uk/Newsletters/newsletter38/03 surfarea. html
 J. Mihel, M. Sikic, S. Tomic, B. Jeren and K. Vlahovicek, PSAIA-Protein Structure and Interaction Analyzer. BMC. Struct. Biol. 8, 21, 2008.
 W. Kabsch and C. Sander, Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers. 22, 2577-2637, 1983.
 P. Fariselli, A. Zauli, I. Rossi, M. Finelli, P. L. Martelli and R. Casadio, A neural network method to improve prediction of protein-protein interaction sites in heterocomplexes. In: 13th IEEE Workshop on Neural Networks for Signal Processing, pp. 33-41. IEEE Press, 2003.
 M. Wei, W. Feifei and P. Xinjun, Prediction of Protein-Protein Interaction Sites Using Support Vector Machine. J. App. Scie. 26, 403-408, 2008.
 Z. Huanxiang and S. Yibing, Prediction of Protein Interaction Sites From Sequence Profile and Residue Neighbor List. Proteins. 44, 336-343, 2001.
 E. Mohammed and S. K. Mohamed, PSO Bounds: A New Hybridization Technique of PSO and EDAs. Stud. Comp. Intell. 203, 509-526, 2009.
 A. Marco, de O. Montes, S. Thomas, B. Mauro and D. Marco, Frankenstein-s PSO: A Composite Particle Swarm Optimization Algorithm. IEEE Trans. Evol. Comput. 13, 1120-1132, 2009.