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BioMed Research International
Volume 2013 (2013), Article ID 686090, 11 pages
An Approach for Identifying Cytokines Based on a Novel Ensemble Classifier
1School of Information Science and Technology, Xiamen University, Xiamen, Fujian, China
2Center for Cloud Computing and Big Data, Xiamen University, Xiamen, Fujian, China
3Shanghai Key Laboratory of Intelligent Information Processing, Shanghai, China
4School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
Received 12 May 2013; Revised 2 July 2013; Accepted 15 July 2013
Academic Editor: Lei Chen
Copyright © 2013 Quan Zou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- Q. Zou, W. C. Chen, Y. Huang, X. R. Liu, and Y. Jiang, “Identifying multi-functional enzyme with hierarchical multi-label classifier,” Journal of Computational and Theoretical Nanoscience, vol. 10, no. 4, pp. 1038–1043, 2013.
- C. Lin, Y. Zou, J. Qin et al., “Hierarchical classification of protein folds using a novel ensemble classifier,” PLoS ONE, vol. 8, no. 2, Article ID e56499, 2013.
- Y. Yabuki, T. Muramatsu, T. Hirokawa, H. Mukai, and M. Suwa, “GRIFFIN: a system for predicting GPCR-G-protein coupling selectivity using a support vector machine and a hidden Markov model,” Nucleic Acids Research, vol. 33, no. 2, pp. W148–W153, 2005.
- P. K. Papasaikas, P. G. Bagos, Z. I. Litou, and S. J. Hamodrakas, “A novel method for GPCR recognition and family classification from sequence alone using signatures derived from profile hidden markov models,” SAR and QSAR in Environmental Research, vol. 14, no. 5-6, pp. 413–420, 2003.
- C.-S. Yu, Y.-C. Chen, C.-H. Lu, and J.-K. Hwang, “Prediction of protein subcellular localization,” Proteins: Structure, Function and Genetics, vol. 64, no. 3, pp. 643–651, 2006.
- H. Nielsen, J. Engelbrecht, S. Brunak, and G. von Heijne, “A neural network method for identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites,” International Journal of Neural Systems, vol. 8, no. 5-6, pp. 581–599, 1997.
- C.-W. Hsu and C.-J. Lin, “A comparison of methods for multiclass support vector machines,” IEEE Transactions on Neural Networks, vol. 13, no. 2, pp. 415–425, 2002.
- S. F. Altschul, W. Gish, W. Miller, E. W. Myers, and D. J. Lipman, “Basic local alignment search tool,” Journal of Molecular Biology, vol. 215, no. 3, pp. 403–410, 1990.
- W. R. Pearson, “Searching protein sequence libraries: comparison of the sensitivity and selectivity of the Smith-Waterman and FASTA algorithms,” Genomics, vol. 11, no. 3, pp. 635–650, 1991.
- G. S. Ladics, G. A. Bannon, A. Silvanovich, and R. F. Cressman, “Comparison of conventional FASTA identity searches with the 80 amino acid sliding window FASTA search for the elucidation of potential identities to known allergens,” Molecular Nutrition and Food Research, vol. 51, no. 8, pp. 985–998, 2007.
- N. Huang, H. Chen, and Z. Sun, “CTKPred: an SVM-based method for the prediction and classification of the cytokine superfamily,” Protein Engineering, Design and Selection, vol. 18, no. 8, pp. 365–368, 2005.
- S. Lata and G. P. S. Raghava, “CytoPred: a server for prediction and classification of cytokines,” Protein Engineering, Design and Selection, vol. 21, no. 4, pp. 279–282, 2008.
- A. Bateman, L. Coin, R. Durbin et al., “The Pfam protein families database,” Nucleic Acids Research, vol. 32, pp. D138–D141, 2004.
- T. Huang, L. Chen, Y.-D. Cai, and K.-C. Chou, “Classification and analysis of regulatory pathways using graph property, biochemical and physicochemical property, and functional property,” PLoS ONE, vol. 6, no. 9, Article ID e25297, 2011.
- H. Altınçay and C. Ergün, “Clustering based under-sampling for improving speaker verification decisions using AdaBoost,” in Structural, Syntactic, and Statistical Pattern Recognition, pp. 698–706, Springer, New York, NY, USA, 2004.
- H. Han, W.-Y. Wang, and B.-H. Mao, “Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning,” in Advances in intelligent computing, vol. 3644 of Lecture Notes in Computer Science, pp. 878–887, Springer, August 2005.
- C. Z. Cai, L. Y. Han, Z. L. Ji, X. Chen, and Y. Z. Chen, “SVM-Prot: web-based support vector machine software for functional classification of a protein from its primary sequence,” Nucleic Acids Research, vol. 31, no. 13, pp. 3692–3697, 2003.
- K.-C. Chou and Y.-D. Cai, “Predicting protein structural class by functional domain composition,” Biochemical and Biophysical Research Communications, vol. 321, no. 4, pp. 1007–1009, 2004.
- A. Bairoch, R. Apweiler, C. H. Wu et al., “The Universal Protein Resource (UniProt),” Nucleic Acids Research, vol. 33, pp. D154–D159, 2005.
- R. Apweiler, A. Bairoch, C. H. Wu et al., “UniProt: the universal protein knowledgebase,” Nucleic Acids Research, vol. 32, supplement 1, pp. D115–D119, 2004.
- C. H. Wu, R. Apweiler, A. Bairoch et al., “The Universal Protein Resource (UniProt): an expanding universe of protein information,” Nucleic Acids Research, vol. 34, supplement 1, pp. D187–D191, 2006.
- Y.-D. Cai, X.-J. Liu, X.-B. Xu, and K.-C. Chou, “Prediction of protein structural classes by support vector machines,” Computers & Chemistry, vol. 26, no. 3, pp. 293–296, 2002.
- H. Nakashima and K. Nishikawa, “Discrimination of intracellular and extracellular proteins using amino acid composition and residue-pair frequencies,” Journal of Molecular Biology, vol. 238, no. 1, pp. 54–61, 1994.
- J. R. Bock and D. A. Gough, “Predicting protein-protein interactions from primary structure,” Bioinformatics, vol. 17, no. 5, pp. 455–460, 2001.
- R. Karchin, K. Karplus, and D. Haussler, “Classifying G-protein coupled receptors with support vector machines,” Bioinformatics, vol. 18, no. 1, pp. 147–159, 2002.
- S. Hua and Z. Sun, “A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach,” Journal of Molecular Biology, vol. 308, no. 2, pp. 397–407, 2001.
- Z. Yuan, K. Burrage, and J. S. Mattick, “Prediction of protein solvent accessibility using Support Vector Machines,” Proteins: Structure, Function and Genetics, vol. 48, no. 3, pp. 566–570, 2002.
- C. H. Q. Ding and I. Dubchak, “Multi-class protein fold recognition using support vector machines and neural networks,” Bioinformatics, vol. 17, no. 4, pp. 349–358, 2001.
- L. Nanni and A. Lumini, “MppS: an ensemble of support vector machine based on multiple physicochemical properties of amino acids,” Neurocomputing, vol. 69, no. 13–15, pp. 1688–1690, 2006.
- Z.-H. Zhou, J. Wu, and W. Tang, “Ensembling neural networks: many could be better than all,” Artificial Intelligence, vol. 137, no. 1-2, pp. 239–263, 2002.
- J. A. Hartigan and M. A. Wong, “Algorithm AS 136: a k-means clustering algorithm,” Journal of the Royal Statistical Society. Series C, vol. 28, no. 1, pp. 100–108, 1979.
- Q. Zou, X. B. Li, Y. Jiang, Y. M. Zhao, and G. H. Wang, “BinMemPredict: a web server and software for predicting membrane protein types,” Current Proteomics, vol. 10, no. 1, pp. 2–9, 2013.