Predicting Protein Interaction Sites Based on a New Integrated Radial Basis Functional Neural Network
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.
[1] W. Xiangyu, C. Shouliang and G. Mingde, General Biology,2rd ed.
Beijing: Higher Education Press, 2005.
[2] G. Drewes and T. Bouwmeester, Global approaches to protein-protein
interactions. Curr.Opin.Cell.Biol. 15, 1-7, 2003.
[3] Y. Changhui, H. Vasant and D. Drena, Predicting Protein-Protein Interaction
Sites From Amino Acid Sequence. Technical report, Iowa State
University, 2002.
[4] F. S'ebastien and Z. Martin, Prediction of Protein-Protein Interaction
Sites Using Electrostatic Desolvation Profiles. Biophys. J. 98, 1921-1930,
2010.
[5] 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.
[6] 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.
[7] L. Man, L. T. Chew and S. Jian, Feature generation and representations
for protein-protein interaction classification. J. Biomed. Inform. 42, 866-
872, 2009.
[8] 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.
[9] 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.
[10] M. M. Gonzalo and S. Alberto, Out-of-bag estimation of the optimal
sample size in bagging. Pattern. Recognit. 43, 143-152, 2010.
[11] S. Noritaka, M. Hiromi, M. Michiharu and M. Lixin, Bagging and
AdaBoost algorithms for vector quantization. Neurocomputing. 73, 106-
114, 2009.
[12] C. Guo and Z. Hongfu, Fusion Diagnosis for EngineWear Fault Based
on Integrated Neural Network. J. Nanjing Univ. Aero & Astr. 36, 278-283,
2004.
[13] Z. Zhihua, W. Jianxin and T. Wei, Ensembling neural networks: Many
could be better than all. Artif. Intell. 137, 239-263, 2002.
[14] 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.
[15] L. Yang, T. Zhengquan and W. Yifei, SVM-based protein interaction
sites prediction. Technical report, Shanghai University, 2006.
[16] L. Chun and Q. Weiyi, Mathematical description of biological macromolecules
and its application. Liaoning: Dalian University of Technology
Press, 2009.
[17] Comparison of SURFACE and AREAIMOL for accessible surface area
calculations, http://www.ccp4.ac.uk/Newsletters/newsletter38/03 surfarea.
html
[18] J. Mihel, M. Sikic, S. Tomic, B. Jeren and K. Vlahovicek, PSAIA-Protein
Structure and Interaction Analyzer. BMC. Struct. Biol. 8, 21, 2008.
[19] W. Kabsch and C. Sander, Dictionary of protein secondary structure:
pattern recognition of hydrogen-bonded and geometrical features.
Biopolymers. 22, 2577-2637, 1983.
[20] 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.
[21] M. Wei, W. Feifei and P. Xinjun, Prediction of Protein-Protein Interaction
Sites Using Support Vector Machine. J. App. Scie. 26, 403-408,
2008.
[22] Z. Huanxiang and S. Yibing, Prediction of Protein Interaction Sites From
Sequence Profile and Residue Neighbor List. Proteins. 44, 336-343, 2001.
[23] E. Mohammed and S. K. Mohamed, PSO Bounds: A New Hybridization
Technique of PSO and EDAs. Stud. Comp. Intell. 203, 509-526, 2009.
[24] 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.
[1] W. Xiangyu, C. Shouliang and G. Mingde, General Biology,2rd ed.
Beijing: Higher Education Press, 2005.
[2] G. Drewes and T. Bouwmeester, Global approaches to protein-protein
interactions. Curr.Opin.Cell.Biol. 15, 1-7, 2003.
[3] Y. Changhui, H. Vasant and D. Drena, Predicting Protein-Protein Interaction
Sites From Amino Acid Sequence. Technical report, Iowa State
University, 2002.
[4] F. S'ebastien and Z. Martin, Prediction of Protein-Protein Interaction
Sites Using Electrostatic Desolvation Profiles. Biophys. J. 98, 1921-1930,
2010.
[5] 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.
[6] 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.
[7] L. Man, L. T. Chew and S. Jian, Feature generation and representations
for protein-protein interaction classification. J. Biomed. Inform. 42, 866-
872, 2009.
[8] 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.
[9] 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.
[10] M. M. Gonzalo and S. Alberto, Out-of-bag estimation of the optimal
sample size in bagging. Pattern. Recognit. 43, 143-152, 2010.
[11] S. Noritaka, M. Hiromi, M. Michiharu and M. Lixin, Bagging and
AdaBoost algorithms for vector quantization. Neurocomputing. 73, 106-
114, 2009.
[12] C. Guo and Z. Hongfu, Fusion Diagnosis for EngineWear Fault Based
on Integrated Neural Network. J. Nanjing Univ. Aero & Astr. 36, 278-283,
2004.
[13] Z. Zhihua, W. Jianxin and T. Wei, Ensembling neural networks: Many
could be better than all. Artif. Intell. 137, 239-263, 2002.
[14] 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.
[15] L. Yang, T. Zhengquan and W. Yifei, SVM-based protein interaction
sites prediction. Technical report, Shanghai University, 2006.
[16] L. Chun and Q. Weiyi, Mathematical description of biological macromolecules
and its application. Liaoning: Dalian University of Technology
Press, 2009.
[17] Comparison of SURFACE and AREAIMOL for accessible surface area
calculations, http://www.ccp4.ac.uk/Newsletters/newsletter38/03 surfarea.
html
[18] J. Mihel, M. Sikic, S. Tomic, B. Jeren and K. Vlahovicek, PSAIA-Protein
Structure and Interaction Analyzer. BMC. Struct. Biol. 8, 21, 2008.
[19] W. Kabsch and C. Sander, Dictionary of protein secondary structure:
pattern recognition of hydrogen-bonded and geometrical features.
Biopolymers. 22, 2577-2637, 1983.
[20] 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.
[21] M. Wei, W. Feifei and P. Xinjun, Prediction of Protein-Protein Interaction
Sites Using Support Vector Machine. J. App. Scie. 26, 403-408,
2008.
[22] Z. Huanxiang and S. Yibing, Prediction of Protein Interaction Sites From
Sequence Profile and Residue Neighbor List. Proteins. 44, 336-343, 2001.
[23] E. Mohammed and S. K. Mohamed, PSO Bounds: A New Hybridization
Technique of PSO and EDAs. Stud. Comp. Intell. 203, 509-526, 2009.
[24] 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.
@article{"International Journal of Information, Control and Computer Sciences:55729", author = "Xiaoli Shen and Yuehui Chen", title = "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.", keywords = "protein interaction sites, features, sliding windows,radial basis functional neutral networks, genetic algorithm basedselected ensemble.", volume = "4", number = "12", pages = "1911-6", }
