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Fuzzy System Methods in Modeling Gene Expression and Analyzing Protein Networks

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Fuzzy Systems in Bioinformatics and Computational Biology

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 242))

Summary

Recent technological advances in high-throughput data collection allow for computational study of complex biological systems on the scale of the whole cellular genome and proteome. Gene regulatory network is expected to be one of suitable tools for interpreting the resulting large amount of genomic and proteomic data sets. A huge number of methods have been developed for extracting gene networks from such data. Fuzzy logic which plays an important role in multiple disciplines is a framework bringing together physics-based models with more logical methods to build a foundation for multi-scale bio-molecular network models. Biological relationships in the best-fitting fuzzy gene network models can successfully recover direct and indirect interactions from previous knowledge to result in more biological insights about regulatory and transcriptional mechanism. In this chapter, we survey a class of models based on fuzzy logic with particular applications in reconstructing gene regulatory networks. We also extend our survey of the application of fuzzy logic methods to highly related topics such as protein interaction network analysis and microarray data analysis. We believe that fuzzy logic-based models would take a key step towards providing a framework for integrating, analyzing and modeling complex biological systems.

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References

  1. Adamcsek, B., Palla, G., Farkas, I.J., Derényi, I., Vicsek, T.: CFinder: Locating cliques and overlapping modules in biological networks. Bioinformatics 22, 1021–1023 (2006)

    Article  Google Scholar 

  2. Akutsu, T., Miyano, S., Kuhara, S.: Identification of genetic networks from a small number of gene expression patterns under the boolean network model. In: Pacific Symposium on Biocomputing, vol. 4, pp. 17–28 (1999)

    Google Scholar 

  3. Belacel, N., Cuperlovic-Culf, M., Laflamme, M., Ouellette, R.: Fuzzy j-means and vns methods for clustering genes from microarray data. Bioinformatics 20, 1690–1701 (2004)

    Article  Google Scholar 

  4. Bezdak, J.C.: Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York (1981)

    Google Scholar 

  5. Carpenter, G.A., Grossberg, S., Rosen, D.B.: Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system. Neural Networks 4, 759–771 (1991)

    Article  Google Scholar 

  6. Combs, W.E., Andrews, J.E.: Combinatorial rule explosion eliminated by a fuzzy rule configuration. IEEE Trans. Fuzzy Syst. 6, 1–11 (1998)

    Article  Google Scholar 

  7. Danon, L., Duch, J., Diaz-Guilera, A., Arenas, A.: Comparing community structure identification. J. Stat. Mech., P09008 (2005)

    Google Scholar 

  8. Datta, S., Sokhansanj, B.A.: Accelerated search for biomolecular network models to interpret high-throughput experimental data. Bioinformatics 8, 258 (2007)

    Article  Google Scholar 

  9. de Jong, H.: Modeling and simulation of genetic regulatory systems: a literature review. J. Comput. Biol. 9, 67–103 (2002)

    Article  Google Scholar 

  10. Dembele, D., Kastner, P.: Fuzzy c-means method for clustering microarray data. Bioinformatics 19(8), 973–980 (2003)

    Article  Google Scholar 

  11. Di Gesu, V., Giancarlo, R., Lo Bosco, G., Raimondi, A., Scaturro, D.: GenClust: a genetic algorithm for clustering gene expression data. BMC Bioinformatics 6, 289 (2005)

    Article  Google Scholar 

  12. Du, P., Gong, J., Wurtele, E.S., Dickerson, J.A.: Modeling gene expression networks using fuzzy logic. IEEE Transactions on Systems, Man, and Cybernetics, Part B 35(6), 1351–1359 (2005)

    Article  Google Scholar 

  13. Eisen, M.B., Spellman, P.T., Brown, P.O., Botstein, D.: Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA 95, 14863–14868 (1998)

    Article  Google Scholar 

  14. Fu, L., Medico, E.: FLAME, a novel fuzzy clustering method for the analysis of DNA microarray data. BMC Bioinformatics 8(3) (2007)

    Google Scholar 

  15. Futschik, M.E., Charlisle, B.: Noise robust clustering of gene expression time-course data. Journal of Bioinformatics and Computational Biology 3(4), 965–988 (2005)

    Article  Google Scholar 

  16. Gasch, A.P., Eisen, M.B.: Exploring the conditional coregulation of yeast gene expression through fuzzy k-means clustering. Genome. Biol. 3(11), 1–22 (2002)

    Article  Google Scholar 

  17. Handl, J., Knowles, J., Kell, D.B.: Computational cluster validation in post-genomic data analysis. Bioinformatics 21(15), 3201–3212 (2005)

    Article  Google Scholar 

  18. Husmeier, D.: Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic bayesian networks. Bioinformatics 19, 2271–2282 (2003)

    Article  Google Scholar 

  19. Jonsson, P.F., Bates, P.A.: Global topological features of cancer proteins in the human interactome. Bioinformatics 22(18), 2291–2297 (2006)

    Article  Google Scholar 

  20. Kim, S.Y., Lee, J.W., Bae, J.S.: Effect of data normalization on fuzzy clustering of DNA microarray data. BMC Bioinformatics 7(134) (2006)

    Google Scholar 

  21. Klir, G.J., Yuan, B. (eds.): Fuzzy sets, fuzzy logics and fuzzy systems — selected papers by lotfi a zadeh. World Scientific, Singapore (1996)

    Google Scholar 

  22. Kosko, B.: Global stability of generalized additive fuzzy systems. Transactions on Systems, Man, and Cybernetics 28(3), 441–452 (1998)

    Article  Google Scholar 

  23. Linden, R., Bhaya, A.: Evolving fuzzy rules to model gene expression. BioSystems 88, 76–91 (2007)

    Article  Google Scholar 

  24. Ma, P.C.H., Chan, K.C.C.: Inference of gene regulatory networks from microarray data: a fuzzy logic approach. In: Proceedings of Asia-Pacific Bioinformatics Conference, pp. 17–26 (2006)

    Google Scholar 

  25. Maraziotis, I., Dragomir, A., Bezerianos, A.: Recurrent neuro-fuzzy network models for reverse engineering gene regulatory interactions. In: Berthold, M.R., Glen, R.C., Diederichs, K., Kohlbacher, O., Fischer, I. (eds.) CompLife 2005. LNCS (LNBI), vol. 3695, pp. 24–34. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  26. Nepusz, T., Petróczi, A., Négyessy, L., Bazsó, F.: Fuzzy communities and the concept of bridgeness in complex networks. Physical Review E 77, 016107 (2008)

    Article  Google Scholar 

  27. Newman, M.E.J.: Detecting community structure in networks. Eur. Phys. J. B. 38, 321–330 (2004)

    Article  Google Scholar 

  28. Palla, G., Derényi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435, 814–818 (2005)

    Article  Google Scholar 

  29. Pascual-Marqui, R.D., Pascual-Montano, A.D., Kochi, K., Carazo, J.M.: Smoothly distributed fuzzy c-means: a new self-organizing map. Pattern Recognition 34, 2395–2402 (2001)

    Article  MATH  Google Scholar 

  30. Qu, Y., Xu, S.: Supervised cluster analysis for microarray data based on multivariate Gaussian mixture. Bioinformatics 20, 1905–1913 (2004)

    Article  Google Scholar 

  31. Ram, R., Chetty, M., Dix, T.I.: Fuzzy model for gene regulatory network. In: IEEE Congress on Evolutionary Computation, pp. 1450–1455 (2006)

    Google Scholar 

  32. Reichardt, J., Bornholdt, S.: Detecting fuzzy community structures in complex networks with a Potts model. Phys. Rev. Lett. 93, 218701 (1993)

    Article  Google Scholar 

  33. Ressom, H., Natarajanb, P., Varghesea, R.S., Musavib, M.T.: Applications of fuzzy logic in genomics. Fuzzy Sets and Systems 152, 125–138 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  34. Ressom, H., Reynolds, R., Varghese, R.S.: Increasing the efficiency of fuzzy logic-based gene expression data analysis. Physiol. Genomics 13, 107–117 (2003)

    Google Scholar 

  35. Ressom, H., Wang, D., Varghese, R.S., Reynolds, R.: Fuzzy logic-based gene regulatory network. In: IEEE International Conference on Fuzzy Systems, vol. 2, pp. 1210–1215 (2003)

    Google Scholar 

  36. Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323–2326 (2000)

    Article  Google Scholar 

  37. Schena, M., Shalon, D., Davis, R.W., Brown, P.O.: Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270, 467–470 (1995)

    Article  Google Scholar 

  38. Sehgal, M.S.B., Gondal, I., Dooley, L.: CF-GeNe: Fuzzy framework for robust gene regulatory network inference. Journal of Computers 1(7), 1–8 (2006)

    Article  Google Scholar 

  39. Sehgal, M.S.B., Gondal, I., Dooley, L., Coppel, R.: AFEGRN: Adaptive fuzzy evolutionary gene regulatory network re-construction framework. In: IEEE International Conference on Fuzzy Systems, pp. 1737–1741 (2006)

    Google Scholar 

  40. Sokhansanj, B.A., Fitch, J.P.: URC fuzzy modeling and simulation of gene regulation. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 3, pp. 2918–2921 (2001)

    Google Scholar 

  41. Sokhansanj, B.A., Fitch, J.P., Quong, J.N., Quong, A.A.: Linear fuzzy gene network models obtained from microarray data by exhaustive search. BMC Bioinformatics 5, 108 (2004)

    Article  Google Scholar 

  42. Takahashi, T., Tomita, S., Kobayashi, T., Honda, H.: Inference on common genetic network using fuzzy art associated matrix method. J. Biosci. Bioeng. 96, 154–160 (2003)

    Google Scholar 

  43. Tamayo, P., Slonim, D., Mesirov, J., Zhu, Q., Kitareewan, S., Dmitrovsky, E., Lander, E.S., Golub, T.R.: Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc. Natl. Acad. Sci. USA 96(96), 2907–2912 (1999)

    Article  Google Scholar 

  44. Torres, A., Nieto, J.J.: Fuzzy logic in medicine and bioinformatics. Journal of Biomedicine and Biotechnology 2006, ARTICLE ID 91908 (2006)

    Article  Google Scholar 

  45. Vinterbo, S.A., Kim, E.Y., Ohno-Machado, L.: Small, fuzzy and interpretable gene expression based classifiers. Bioinformatics 21, 1964–1970 (2005)

    Article  Google Scholar 

  46. Woolf, P.J., Wang, Y.: A fuzzy logic approach to analyzing gene expression data. Physiol. Genomics 3, 9–15 (2000)

    Google Scholar 

  47. Xing, E.P., Karp, R.M.: CLIFF: clustering of high-dimensional microarray data via iterative feature filtering using normalized cuts. Bioinformatics 17(S1), S306–S315 (2001)

    Google Scholar 

  48. Yeung, K.Y., Fraley, C., Murua, A., Raftery, E., Ruzzo, W.L.: Model-based clustering and data transformations for gene expression data. Bioinformatics 17(10), 977–987 (2001)

    Article  Google Scholar 

  49. Yeung, K.Y., Haynor, D.R., Ruzzo, W.L.: Validating clustering for gene expression data. bioinformatics. bioinformatics 17(4), 309–318 (2001)

    Article  Google Scholar 

  50. Zadeh, L.A.: Fuzzy sets. Information and Control 8, 338–353 (1965)

    Article  MATH  MathSciNet  Google Scholar 

  51. Zhang, S., Jin, G., Zhang, X.S., Chen, L.: Discovering functions and revealing mechanisms at molecular level from biological networks. Proteomics 7(16), 2856–2869 (2007)

    Article  Google Scholar 

  52. Zhang, S., Liu, H.W., Ning, X.M., Zhang, X.S.: A hybrid graph-theoretic method for mining overlapping functional modules in large sparse protein interaction networks. International Journal of Data Mining and Bioinformatics (in press)

    Google Scholar 

  53. Zhang, S., Wang, R.S., Zhang, X.S.: Identification of overlapping community structure in complex networks using fuzzy c-means clustering. Physica A 374(1), 483–490 (2007)

    Article  Google Scholar 

  54. Zhang, S., Wang, R.S., Zhang, X.S.: Uncovering fuzzy community structure in complex networks. Physical Review E 76, 046103 (2007)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100080, China

    Shihua Zhang & Xiang-Sun Zhang

  2. Graduate University of Chinese Academy of Sciences, Beijing, 100049, China

    Shihua Zhang

  3. School of Information, Renmin University of China, Beijing, 100872, China

    Rui-Sheng Wang

  4. Institute of Systems Biology, Shanghai University, Shanghai, 200444, China

    Luonan Chen

  5. Department of Electrical Engineering and Electronics, Osaka Sangyo University, Osaka, 574-8530, Japan

    Rui-Sheng Wang & Luonan Chen

Authors
  1. Shihua Zhang
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  2. Rui-Sheng Wang
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  3. Xiang-Sun Zhang
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  4. Luonan Chen
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Editor information

Editors and Affiliations

  1. Honda Research Institute Honda Research Institute Europe , Offenbach, Germany

    Yaochu Jin

  2. Nanyang Technological University, Singapore

    Lipo Wang

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© 2009 Springer-Verlag Berlin Heidelberg

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Zhang, S., Wang, RS., Zhang, XS., Chen, L. (2009). Fuzzy System Methods in Modeling Gene Expression and Analyzing Protein Networks. In: Jin, Y., Wang, L. (eds) Fuzzy Systems in Bioinformatics and Computational Biology. Studies in Fuzziness and Soft Computing, vol 242. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89968-6_9

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  • DOI: https://doi.org/10.1007/978-3-540-89968-6_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89967-9

  • Online ISBN: 978-3-540-89968-6

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