Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2015 (2015), Article ID 902198, 10 pages
http://dx.doi.org/10.1155/2015/902198
Research Article

Using Weighted Sparse Representation Model Combined with Discrete Cosine Transformation to Predict Protein-Protein Interactions from Protein Sequence

1College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China
2School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China
3Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Suzhou, Jiangsu 215163, China
4School of Electronic and Information Engineering, Soochow University, Suzhou, Jiangsu 215123, China

Received 13 August 2015; Accepted 4 October 2015

Academic Editor: Alok Sharma

Copyright © 2015 Yu-An Huang 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.

Linked References

  1. A.-C. Gavin, M. Bösche, R. Krause et al., “Functional organization of the yeast proteome by systematic analysis of protein complexes,” Nature, vol. 415, no. 6868, pp. 141–147, 2002. View at Publisher · View at Google Scholar
  2. T. Ito, T. Chiba, R. Ozawa, M. Yoshida, M. Hattori, and Y. Sakaki, “A comprehensive two-hybrid analysis to explore the yeast protein interactome,” Proceedings of the National Academy of Sciences of the United States of America, vol. 98, no. 8, pp. 4569–4574, 2001. View at Publisher · View at Google Scholar · View at Scopus
  3. Y. Ho, A. Gruhler, A. Heilbut et al., “Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry,” Nature, vol. 415, no. 6868, pp. 180–183, 2002. View at Publisher · View at Google Scholar
  4. F. Pazos and A. Valencia, “In silico two-hybrid system for the selection of physically interacting protein pairs,” Proteins: Structure, Function & Genetics, vol. 47, no. 2, pp. 219–227, 2002. View at Publisher · View at Google Scholar · View at Scopus
  5. A. Zanzoni, L. Montecchi-Palazzi, M. Quondam, G. Ausiello, M. Helmer-Citterich, and G. Cesareni, “MINT: a molecular INTeraction database,” FEBS Letters, vol. 513, no. 1, pp. 135–140, 2002. View at Publisher · View at Google Scholar · View at Scopus
  6. G. D. Bader, D. Betel, and C. W. V. Hogue, “BIND: the biomolecular interaction network database,” Nucleic Acids Research, vol. 31, no. 1, pp. 248–250, 2003. View at Publisher · View at Google Scholar · View at Scopus
  7. I. Xenarios, Ł. Salwínski, X. J. Duan, P. Higney, S.-M. Kim, and D. Eisenberg, “DIP, the database of interacting proteins: a research tool for studying cellular networks of protein interactions,” Nucleic Acids Research, vol. 30, no. 1, pp. 303–305, 2002. View at Publisher · View at Google Scholar · View at Scopus
  8. Z.-H. You, Y.-K. Lei, J. Gui, D.-S. Huang, and X. Zhou, “Using manifold embedding for assessing and predicting protein interactions from high-throughput experimental data,” Bioinformatics, vol. 26, no. 21, Article ID btq510, pp. 2744–2751, 2010. View at Publisher · View at Google Scholar · View at Scopus
  9. Z.-H. You, Z. Yin, K. Han, D.-S. Huang, and X. Zhou, “A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties of functional gene network,” BMC Bioinformatics, vol. 11, article 343, 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. Z. H. You, L. Zhu, C. H. Zheng, H. Yu, S. Deng, and Z. Ji, “Prediction of protein-protein interactions from amino acid sequences using a novel multi-scale continuous and discontinuous feature set,” BMC Bioinformatics, vol. 15, supplement 15, p. S9, 2014. View at Publisher · View at Google Scholar
  11. J. Lyons, A. Dehzangi, R. Heffernan et al., “Advancing the accuracy of protein fold recognition by utilizing profiles from hidden Markov models,” IEEE Transactions on NanoBioscience, 2015. View at Publisher · View at Google Scholar
  12. A. Dehzangi, A. Sharma, J. Lyons, K. K. Paliwal, and A. Sattar, “A mixture of physicochemical and evolutionarybased feature extraction approaches for protein fold recognition,” International Journal of Data Mining and Bioinformatics, vol. 11, no. 1, pp. 115–138, 2014. View at Publisher · View at Google Scholar · View at Scopus
  13. A. Fujita, “Identification of altered gene regulatory networks in gliomas by using gene network entropy analysis,” in Proceedings of the International Symposium on Tumor Biology in Kanazawa Symposium on Drug Discoverry in Academics: Program and Abstracts, pp. 32–33, Kanazawa, Japan, January 2014.
  14. A. Dehzangi, R. Heffernan, A. Sharma, J. Lyons, K. Paliwal, and A. Sattar, “Gram-positive and Gram-negative protein subcellular localization by incorporating evolutionary-based descriptors into Chou's general PseAAC,” Journal of Theoretical Biology, vol. 364, pp. 284–294, 2015. View at Publisher · View at Google Scholar · View at Scopus
  15. S. T. Roweis and L. K. Saul, “Nonlinear dimensionality reduction by locally linear embedding,” Science, vol. 290, no. 5500, pp. 2323–2326, 2000. View at Publisher · View at Google Scholar · View at Scopus
  16. H. Saini, G. Raicar, A. Sharma et al., “Probabilistic expression of spatially varied amino acid dimers into general form of Chou's pseudo amino acid composition for protein fold recognition,” Journal of Theoretical Biology, vol. 380, pp. 291–298, 2015. View at Publisher · View at Google Scholar
  17. T. Jaakkola M, M. Diekhans, and D. Haussler, “Using the Fisher kernel method to detect remote protein homologies,” in Proceedings of the International Conference on Intelligent Systems for Molecular Biology, pp. 149–158, 1999.
  18. E. C. Leslie and W.-S. Noble, “The spectrum kernel: a string kernel for SVM protein classification,” Pacific Symposium on Biocomputing, pp. 564–575, 2002. View at Google Scholar
  19. C. S. Leslie, E. Eskin, A. Cohen, J. Weston, and W. S. Noble, “Mismatch string kernels for discriminative protein classification,” Bioinformatics, vol. 20, no. 4, pp. 467–476, 2004. View at Publisher · View at Google Scholar · View at Scopus
  20. B. Liu, D. Zhang, R. Xu et al., “Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection,” Bioinformatics, vol. 30, no. 4, pp. 472–479, 2014. View at Publisher · View at Google Scholar · View at Scopus
  21. L. Zhu, Z.-H. You, and D.-S. Huang, “Increasing the reliability of protein–protein interaction networks via non-convex semantic embedding,” Neurocomputing, vol. 121, pp. 99–107, 2013. View at Publisher · View at Google Scholar · View at Scopus
  22. X. Luo, Z.-H. You, M.-C. Zhou et al., “A highly efficient approach to protein interactome mapping based on collaborative filtering framework,” Scientific Reports, vol. 5, no. 7702, 2015. View at Publisher · View at Google Scholar
  23. B. Liu, J. Xu, Q. Zou, R. Xu, X. Wang, and Q. Chen, “Using distances between Top-n-gram and residue pairs for protein remote homology detection,” BMC Bioinformatics, vol. 15, supplement 2, article S3, 2014. View at Google Scholar · View at Scopus
  24. Z.-H. You, J.-Z. Yu, L. Zhu, S. Li, and Z.-K. Wen, “A MapReduce based parallel SVM for large-scale predicting protein-protein interactions,” Neurocomputing, vol. 145, no. 18, pp. 37–43, 2014. View at Publisher · View at Google Scholar · View at Scopus
  25. Y.-Z. Guo, L.-Z. Yu, Z.-N. Wen, and M. Li, “Using support vector machine combined with auto covariance to predict protein–protein interactions from protein sequences,” Nucleic Acids Research, vol. 36, no. 9, pp. 3025–3030, 2008. View at Publisher · View at Google Scholar · View at Scopus
  26. Y.-Z. Zhou, Y. Gao, and Y.-Y. Zheng, “Prediction of protein-protein interactions using local description of amino acid sequence,” in Advances in Computer Science and Education Applications, vol. 202 of Communications in Computer and Information Science, pp. 254–262, Springer, Berlin, Germany, 2011. View at Publisher · View at Google Scholar
  27. L. Yang, J.-F. Xia, and J. Gui, “Prediction of protein-protein interactions from protein sequence using local descriptors,” Protein and Peptide Letters, vol. 17, no. 9, pp. 1085–1090, 2010. View at Publisher · View at Google Scholar · View at Scopus
  28. L. Wong, Z.-H. You, S. Li, Y.-A. Huang, and G. Liu, “Detection of protein-protein interactions from amino acid sequences using a rotation forest model with a novel PR-LPQ descriptor,” in Advanced Intelligent Computing Theories and Applications, vol. 9227 of Lecture Notes in Computer Science, pp. 713–720, Springer, Berlin, Germany, 2015. View at Publisher · View at Google Scholar
  29. Z.-H. You, Y.-K. Lei, L. Zhu, J. Xia, and B. Wang, “Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis,” BMC Bioinformatics, vol. 14, supplement 8, article S10, 2013. View at Publisher · View at Google Scholar · View at Scopus
  30. J. R. Bock and D. A. Gough, “Whole-proteome interaction mining,” Bioinformatics, vol. 19, no. 1, pp. 125–134, 2003. View at Publisher · View at Google Scholar · View at Scopus
  31. L. Nanni, “Hyperplanes for predicting protein-protein interactions,” Neurocomputing, vol. 69, no. 1, pp. 257–263, 2005. View at Publisher · View at Google Scholar · View at Scopus
  32. S. Martin, D. Roe, and J.-L. Faulon, “Predicting protein-protein interactions using signature products,” Bioinformatics, vol. 21, no. 2, pp. 218–226, 2005. View at Publisher · View at Google Scholar · View at Scopus
  33. L. Nanni and A. Lumini, “An ensemble of K-local hyperplanes for predicting protein–protein interactions,” Bioinformatics, vol. 22, no. 10, pp. 1207–1210, 2006. View at Publisher · View at Google Scholar · View at Scopus
  34. B. Liu, J. Yi, S. V. Aishwarya et al., “QChIPat: a quantitative method to identify distinct binding patterns for two biological ChIP-seq samples in different experimental conditions,” BMC Genomics, vol. 14, no. 8, article S3, 2013. View at Publisher · View at Google Scholar · View at Scopus
  35. B. Liu, F. Liu, L. Fang, X. Wang, and K. Chou, “repDNA: a python package to generate various modes of feature vectors for DNA sequences by incorporating user-defined physicochemical properties and sequence-order effects,” Bioinformatics, vol. 31, no. 8, pp. 1307–1309, 2015. View at Publisher · View at Google Scholar
  36. X. Yu, X. Zheng, T. Liu, Y. Dou, and J. Wang, “Predicting subcellular location of apoptosis proteins with pseudo amino acid composition: approach from amino acid substitution matrix and auto covariance transformation,” Amino Acids, vol. 42, no. 5, pp. 1619–1625, 2012. View at Publisher · View at Google Scholar · View at Scopus
  37. N. Ahmed, T. Natarajan, and K. Rao, “Discrete cosine transform,” IEEE Transactions on Computers, vol. 23, no. 1, pp. 90–93, 1974. View at Publisher · View at Google Scholar
  38. J. Wright, A. Ganesh, Z. Zhou, A. Wagner, and Y. Ma, “Demo: robust face recognition via sparse representation,” in Proceedings of the 8th IEEE International Conference on Automatic Face & Gesture Recognition (FG '08), pp. 1–2, IEEE, Amsterdam, The Netherlands, September 2008. View at Publisher · View at Google Scholar · View at Scopus
  39. B. Liao, Y. Jiang, G. Yuan, W. Zhu, L. Cai, and Z. Cao, “Learning a weighted meta-sample based parameter free sparse representation classification for microarray data,” PLoS ONE, vol. 9, no. 8, Article ID e104314, 2014. View at Publisher · View at Google Scholar · View at Scopus
  40. J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong, “Locality-constrained linear coding for image classification,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '10), pp. 3360–3367, San Francisco, Calif, USA, June 2010. View at Publisher · View at Google Scholar · View at Scopus
  41. A. Sharma and K. K. Paliwal, “A deterministic approach to regularized linear discriminant analysis,” Neurocomputing, vol. 151, no. 1, pp. 207–214, 2015. View at Publisher · View at Google Scholar · View at Scopus
  42. C.-Y. Lu, H. Min, J. Gui, L. Zhu, and Y.-K. Lei, “Face recognition via weighted sparse representation,” Journal of Visual Communication and Image Representation, vol. 24, no. 2, pp. 111–116, 2013. View at Publisher · View at Google Scholar · View at Scopus
  43. D. N. Georgiou, T. E. Karakasidis, J. J. Nieto, and A. Torres, “A study of entropy/clarity of genetic sequences using metric spaces and fuzzy sets,” Journal of Theoretical Biology, vol. 267, no. 1, pp. 95–105, 2010. View at Publisher · View at Google Scholar · View at Scopus
  44. J. J. Nieto, A. Torres, D. N. Georgiou, and T. E. Karakasidis, “Fuzzy polynucleotide spaces and metrics,” Bulletin of Mathematical Biology, vol. 68, no. 3, pp. 703–725, 2006. View at Publisher · View at Google Scholar · View at Scopus
  45. B. Liu, L. Fang, F. Liu et al., “Identification of real microRNA precursors with a pseudo structure status composition approach,” PLoS ONE, vol. 10, no. 3, Article ID e0121501, 2015. View at Publisher · View at Google Scholar