The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.01 - Jan.-Feb. (2013 vol.10)
pp: 219-225
Yuan Zhu , Dept. of Math., Guangdong Univ. of Bus. Studies, Guangzhou, China
Xiao-Fei Zhang , Dept. of Math., Sun Yat-Sen Univ., Guangzhou, China
Dao-Qing Dai , Dept. of Math., Sun Yat-Sen Univ., Guangzhou, China
Meng-Yun Wu , Dept. of Math., Sun Yat-Sen Univ., Guangzhou, China
ABSTRACT
With the rapid development of high-throughput experiment techniques for protein-protein interaction (PPI) detection, a large amount of PPI network data are becoming available. However, the data produced by these techniques have high levels of spurious and missing interactions. This study assigns a new reliably indication for each protein pairs via the new generative network model (RIGNM) where the scale-free property of the PPI network is considered to reliably identify both spurious and missing interactions in the observed high-throughput PPI network. The experimental results show that the RIGNM is more effective and interpretable than the compared methods, which demonstrate that this approach has the potential to better describe the PPI networks and drive new discoveries.
INDEX TERMS
Proteins, Reliability, Biological system modeling, Humans, Data models, Noise measurement,PPI data denoising, Protein-protein interaction network, generative network model
CITATION
Yuan Zhu, Xiao-Fei Zhang, Dao-Qing Dai, Meng-Yun Wu, "Identifying Spurious Interactions and Predicting Missing Interactions in the Protein-Protein Interaction Networks via a Generative Network Model", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.10, no. 1, pp. 219-225, Jan.-Feb. 2013, doi:10.1109/TCBB.2012.164
REFERENCES
[1] T. Ito, T. Chiba, R. Ozawa, M. Yoshida, M. Hattori, and Y. Sakaki, “A Comprehensive Two-Hybrid Analysis to Explore the Yeast Protein Interactome,” Proc. Nat'l Academy of Sciences USA, vol. 98, no. 8, pp. 4569-4574, 2001.
[2] P. Uetz et al., “A Comprehensive Analysis of Protein-Protein Interactions in Saccharomyces Cerevisiae,” Nature, vol. 403, no. 6770, pp. 623-627, 2000.
[3] S. Collins, P. Kemmeren, X.C. Zhao, J.F. Greenblatt, F. Spencer, F.C.P. Holstege, J.S. Weissman, and N.J. Krogan, “Toward a Comprehensive Atlas of the Physical Interactome of Saccharomyces Cerevisiae,” Molecular and Cellular Proteomics, vol. 6, no. 3, pp. 439-450, 2007.
[4] J.P. Miller, R.S. Lo, A. Ben-Hur, C. Desmarais, I. Stagljar, W.S. Noble, and S. Fields, “Large-Scale Identification of Yeast Integral Membrane Protein Interactions,” Proc. Nat'l Academy of Sciences USA, vol. 102, no. 34, pp. 12123-12128, 2005.
[5] Y. Ho et al., “Systematic Identification of Protein Complexes in Saccharomyces Cerevisiae by Mass Spectrometry,” Nature, vol. 415, no. 6868, pp. 180-183, 2002.
[6] X.F. Zhang and D.Q. Dai, “A Framework for Incorporating Functional Inter-Relationships into Protein Function Prediction Algorithms,” IEEE/ACM Trans. Computational Biology and Bioinformatics, vol. 9, no. 3, pp. 740-753, May/June 2012.
[7] C.von Mering, R. Krause, B. Snel, M. Cornell, S.G. Oliver, S. Fields, and P. Bork, “Comparative Assessment of Large-Scale Data Sets of Protein-Protein Interactions,” Nature, vol. 417, no. 6887, pp. 399-404, 2002.
[8] A.M. Edwards, B. Kus, R. Jansen, D. Greenbaum, J. Greenblatt, and M. Gerstein, “Bridging Structural Biology and Genomics: Assessing Protein Interaction Data with Known Complexes,” Trends in Genetics, vol. 18, no. 10, pp. 529-536, 2002.
[9] S. Suthram, T. Shlomi, E. Ruppin, R. Sharan, and T. Ideker, “A Direct Comparison of Protein Interaction Confidence Assignment Schemes,” BMC Bioinformatics, vol. 7, no. 1, article 360, 2006.
[10] J.S. Bader, A. Chaudhuri, J.M. Rothberg, and J. Chant, “Gaining Confidence in High-Throughput Protein Interaction Networks,” Nature Biotechnology, vol. 22, no. 1, pp. 78-85, 2003.
[11] R. Sharan, S. Suthram, R. Kelley, T. Kuhn, S. McCuine, P. Uetz, T. Sittler, R.M. Karp, and T. Ideker, “Conserved Patterns of Protein Interaction in Multiple Species,” Proc. Nat'l Academy of Sciences USA, vol. 102, no. 6, pp. 1974-1979, 2005.
[12] G. Bebek and J. Yang, “Pathfinder: Mining Signal Transduction Pathway Segments from Protein-Protein Interaction Networks,” BMC Bioinformatics, vol. 8, no. 1, article 335, 2007.
[13] R. Jansen, H. Yu, D. Greenbaum, Y. Kluger, N.J. Krogan, S. Chung, A. Emili, M. Snyder, J.F. Greenblatt, and M. Gerstein, “A Bayesian Networks Approach for Predicting Protein-Protein Interactions from Genomic Data,” Science, vol. 302, no. 5644, pp. 449-453, 2003.
[14] J. Chen, W. Hsu, M.L. Lee, and S.K. Ng, “Increasing Confidence of Protein Interactomes Using Network Topological Metrics,” Bioinformatics, vol. 22, no. 16, pp. 1998-2004, 2006.
[15] H.N. Chua, W.K. Sung, and L. Wong, “Exploiting Indirect Neighbours and Topological Weight to Predict Protein Function from Protein-Protein Interactions,” Bioinformatics, vol. 22, no. 13, pp. 1623-1630, 2006.
[16] R. Saito, H. Suzuki, and Y. Hayashizaki, “Construction of Reliable Protein-Protein Interaction Networks with a New Interaction Generality Measure,” Bioinformatics, vol. 19, no. 6, pp. 756-763, 2003.
[17] A.M. Yip and S. Horvath, “Gene Network Interconnectedness and the Generalized Topological Overlap Measure,” BMC Bioinformatics, vol. 8, no. 1, article 22, 2007.
[18] H.N. Chua and L. Wong, “Increasing the Reliability of Protein Interactomes,” Drug Discovery Today, vol. 13, nos. 15/16, pp. 652-658, 2008.
[19] 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, pp. 2744-2751, 2010.
[20] Y. Fang, W. Benjamin, M. Sun, and K. Ramani, “Global Geometric Affinity for Revealing High Fidelity Protein Interaction Network,” PLoS One, vol. 6, no. 5, p. e19349, 2011.
[21] A.L. Barabási and R. Albert, “Emergence of Scaling in Random Networks,” Science, vol. 286, no. 5439, pp. 509-512, 1999.
[22] D.J. Higham, M. Rašajski, and N. Pržulj, “Fitting a Geometric Graph to a Protein-Protein Interaction Network,” Bioinformatics, vol. 24, no. 8, pp. 1093-1099, 2008.
[23] F. Hormozdiari, P. Berenbrink, N. Pržulj, and S.C. Sahinalp, “Not All Scale-Free Networks Are Born Equal: The Role of the Seed Graph in PPI Network Evolution,” PLoS Computational Biology, vol. 3, no. 7, p. e118, 2007.
[24] N. Pržulj, D.G. Corneil, and I. Jurisica, “Modeling Interactome: Scale-Free or Geometric?” Bioinformatics, vol. 20, no. 18, pp. 3508-3515, 2004.
[25] J.M.O. Ranola, S. Ahn, M. Sehl, D. Smith, and K. Lange, “A Poisson Model for Random Multigraphs,” Bioinformatics, vol. 26, no. 16, pp. 2004-2011, 2010.
[26] R. Schweiger, M. Linial, and N. Linial, “Generative Probabilistic Models for Protein-Protein Interaction Networks the Biclique Perspective,” Bioinformatics, vol. 27, no. 13, pp. i142-i148, 2011.
[27] R. Guimerà and M. Sales-Pardo, “Missing and Spurious Interactions and the Reconstruction of Complex Networks,” Proc. Nat'l Academy of Sciences USA, vol. 106, no. 52, pp. 22073-22078, 2009.
[28] O. Kuchaiev, M. Rašajski, D.J. Higham, and N. Pržulj, “Geometric De-Noising of Protein-Protein Interaction Networks,” PLoS Computational Biology, vol. 5, no. 8, p. e1000454, 2009.
[29] X.F. Zhang, D.Q. Dai, and X.X. Li, “Protein Complexes Discovery Based on Protein-Protein Interaction Data via a Regularized Sparse Generative Network Model,” IEEE/ACM Trans. Computational Biology and Bioinformatics, vol. 9, no. 3, pp. 857-870, May/June 2012.
[30] Z.M. Saul and V. Filkov, “Exploring Biological Network Structure Using Exponential Random Graph Models,” Bioinformatics, vol. 23, no. 19, pp. 2604-2611, 2007.
[31] E.M. Airoldi, D.M. Blei, S.E. Fienberg, and E.P. Xing, “Mixed Membership Stochastic Blockmodels,” J. Machine Learning Research, vol. 9, pp. 1981-2014, 2008.
[32] A.L. Barabási and Z.N. Oltvai, “Network Biology: Understanding the Cell's Functional Organization,” Nature Rev. Genetics, vol. 5, no. 2, pp. 101-113, 2004.
[33] V.Y.F. Tan and C. Févotte, “Automatic Relevance Determination in Nonnegative Matrix Factorization,” Proc. Signal Processing with Adaptive Sparse Structured Representations (SPARS '09), 2009.
[34] B. Ball, B. Karrer, and M.E.J. Newman, “Efficient and Principled Method for Detecting Communities in Networks,” Physical Rev. E, vol. 84, p. 036103, 2011.
[35] M. Yuan and Y. Lin, “Model Selection and Estimation in Regression with Grouped Variables,” J. Royal Statistical Soc., vol. 68, no. 1, pp. 49-67, 2006.
[36] C. Stark, B.J. Breitkreutz, T. Reguly, L. Boucher, A. Breitkreutz, and M. Tyers, “BioGRID: A General Repository for Interaction Data Sets,” Nucleic Acids Research, vol. 34, no. suppl. 1, pp. D535-D539, 2006.
[37] P. Shannon, A. Markiel, O. Ozier, N. Baliga, J. Wang, D. Ramage, N. Amin, B. Schwikowski, and T. Ideker, “Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks,” Genome Research, vol. 13, no. 11, pp. 2498-2504, 2003.
[38] D.D. Lee and H.S. Seung, “Algorithms for Non-Negative Matrix Factorization,” Proc. Advances in Neural Information Processing Systems, vol. 13, 2001.
[39] H.W. Kuhn and A.W. Tucker, “Nonlinear Programming,” Proc. Second Berkeley Symp. Math. Statistics and Probability, vol. 5, 1951.
[40] M. Ashburner et al., “Gene Ontology: Tool for the Unification of Biology,” Nature Genetics, vol. 25, no. 1, pp. 25-29, 2000.
[41] R. Edgar, M. Domrachev, and A.E. Lash, “Gene Expression Omnibus: NCBI Gene Expression and Hybridization Array Data Repository,” Nucleic Acids Research, vol. 30, no. 1, pp. 207-210, 2002.
[42] T.S.K. Prasad et al., “Human Protein Reference Database 2009 Update,” Nucleic Acids Research, vol. 37, no. suppl. 1, pp. D767-D772, 2009.
[43] C. Brun, F. Chevenet, D. Martin, J. Wojcik, A. Guénoche, and B. Jacq, “Functional Classification of Proteins for the Prediction of Cellular Function from a Protein-Protein Interaction Network,” Genome Biology, vol. 5, no. 1, p. R6, 2004.
[44] S. Oliver et al., “Guilt-by-Association Goes Global,” Nature, vol. 403, no. 6770, pp. 601-603, 2000.
[45] H. Ge, Z. Liu, G.M. Church, and M. Vidal, “Correlation Between Transcriptome and Interactome Mapping Data from Saccharomyces Cerevisiae,” Nature Genetics, vol. 29, no. 4, pp. 482-486, 2001.
29 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool