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Inferring Gene Regulatory Networks from Expression Data

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Computational Intelligence in Bioinformatics

Part of the book series: Studies in Computational Intelligence ((SCI,volume 94))

Summary

Gene regulatory networks describe how cells control the expression of genes, which, together with some additional regulation further downstream, determines the production of proteins essential for cellular function. The level of expression of each gene in the genome is modified by controlling whether and how vigorously it is transcribed to RNA, and subsequently translated to protein. RNA and protein expression will influence expression rates of other genes, thus giving rise to a complicated network structure.

An analysis of regulatory processes within the cell will significantly further our understanding of cellular dynamics. It will shed light on normal and abnormal, diseased cellular events, and may provide information on pathways in dire diseases such as cancer. These pathways can provide information on how the disease develops, and what processes are involved in progression. Ultimately, we can hope that this will provide us with new therapeutic approaches and targets for drug design.

It is thus no surprise that many efforts have been undertaken to reconstruct gene regulatory networks from gene expression measurements. In this chapter, we will provide an introductory overview over the field. In particular, we will present several different approaches to gene regulatory network inference, discuss their strengths and weaknesses, and provide guidelines on which models are appropriate under what circumstances. In addition, we sketch future developments and open problems.

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References

  1. H. Akaike, A new look at the statistical model identification, IEEE Trans. Automatic Control 19 (1974), 716–723.

    Article  MathSciNet  MATH  Google Scholar 

  2. T. Akutsu, S. Miyano, and S. Kuhara, Identification of genetic networks from a small number of gene expression patterns under the boolean network model, Pac Symp Biocomput 4 (1999), 17–28.

    Google Scholar 

  3. T. Akutsu, S. Miyano, and S. Kuhara, Algorithms for identifying boolean networks and related biological networks based on matrix multiplication and fingerprint function, RECOMB’00: Proceedings of the fourth annual international conference on Computational molecular biology (New York, NY, USA), ACM Press, 2000, pp. 8–14.

    Google Scholar 

  4. R. Albert and H.G. Othmer, The topology of the regulatory interactions predict the expression pattern of the segment polarity genes in Drosophila melanogaster, J Theor Biol 223 (2003), 1–18.

    Article  MathSciNet  Google Scholar 

  5. B. Alberts, A. Johnson, J. Lewis, M. Raff, K. Roberts, and P. Walter (eds.), Molecular biology of the cell, 4 ed., Garland Publishing, New York, 2002.

    Google Scholar 

  6. U. Alon, An introduction to systems biology - design principles of biological circuits, Chapman & Hall/CRC Mathematical and Computational Biology Series, New York, 2007.

    Google Scholar 

  7. A. Arkin, J. Ross, and H.H. McAdams, Stochastic kinetic analysis of developmental pathway bifurcation in phage λ - infected Escherichia coli cells, Genetics 149 (1998), no. 4, 1633–1648.

    Google Scholar 

  8. J. Bähler, Cell-cycle control of gene expression in budding and fission yeast, Annu. Rev. Genet. 39 (2005), 69–94.

    Article  Google Scholar 

  9. M. Bansal, V. Belcastro, A. Ambesi-Impiombato, and D. di Bernardo, How to infer gene networks from expression profiles, Molecular Systems Biology 3 (2007), 78.

    Google Scholar 

  10. M. Bansal, G.D. Gatta, and D. di Bernardo, Inference of gene regulatory networks and compound mode of action from time course gene expression profiles, Bioinformatics 22 (2006), no. 7, 815–822.

    Article  Google Scholar 

  11. K. Basso, A.A. Margolin, G. Stolovitzky, U. Klein, R. Dalla-Favera, and A. Califano, Reverse engineering of regulatory networks in human B-cells, Nature Genetics 37 (2005), 382–390.

    Article  Google Scholar 

  12. J. Beirlant, E. Dudewicz, L. Gyorfi, and E. van der Meulen, Nonparameteric entropy estimation: An overview, Int J Math Stat Sci 6 (1997), no. 1, 17–39.

    MathSciNet  MATH  Google Scholar 

  13. P. Berg and M. Singer (eds.), Dealing with genes, University Science books, 1992.

    Google Scholar 

  14. A. Bernard and J. Hartemink, Informative structure priors: Joint learning of dynamic regulatory networks from multiple types of data, Pac Symp Biocomput (2005), 459–70.

    Google Scholar 

  15. H. Bolouri and E.H. Davidson, Modeling transcriptional regulatory networks, BioEssays 24 (2002), 1118–1129.

    Article  Google Scholar 

  16. S. Bornholdt, Less is more in modeling large genetic networks, Science 310 (2005), no. 5747, 449–450.

    Article  Google Scholar 

  17. E. Boros, T. Ibaraki, and K. Makino, Error-free and best-fit extension of partially defined boolean functions, Information and Computation 140 (1998), 254–283.

    Article  MathSciNet  MATH  Google Scholar 

  18. S. Bulashevska and R. Eils, Inferring genetic regulatory logic from expression data, Bioinformatics 21 (2005), no. 11, 2706–2713.

    Article  Google Scholar 

  19. W. Buntine, Theory refinement on bayesian networks, Proceedings of the 7th Conference on Uncertainty in Artificial Intelligence (Los Angeles, CA, USA) (B. D’Ambrosio, P. Smets, and P. Bonissone, eds.), Morgan Kaufmann Publishers, 1991, pp. 52–60.

    Google Scholar 

  20. A. Butte and I. Kohane, Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements, Pac Symp Biocomput, 2000, pp. 418–429.

    Google Scholar 

  21. A.J. Butte, P. Tamayo, D. Slonim, T.R. Golub, and I.S. Kohane, Discovering functional relationships between rna expression and chemotherapeutic susceptibility using relevance networks, Proc Natl Acad Sci U S A 97 (2000), no. 22, 12182–12186.

    Article  Google Scholar 

  22. K.-C. Chen, T.-Y. Wang, H.-H. Tseng, C.-Y.F. Huang, and C.-Y. Kao, A stochastic differential equation model for quantifying transcriptional regulatory network in Saccharomyces cerevisiae, Bioinformatics 21 (2005), no. 12, 2883–2890.

    Article  Google Scholar 

  23. L. Chen and K. Aihara, A model for periodic oscillation for genetic regulatory systems, IEEE Trans. Circuits and Systems I 49 (2002), no. 10, 1429–1436.

    Article  MathSciNet  Google Scholar 

  24. T. Chen, H.L. He, and G.M. Church, Modeling gene expression with differential equations, Pac Symp Biocomput, 1999, pp. 29–40.

    Google Scholar 

  25. D.M. Chickering, D. Geiger, and D. Heckerman, Learning bayesian networks: Search methods and experimental results, Proceedings of the Fifth Conference on Artificial Intelligence and Statistics (Ft. Lauderdale), Society for Artificial Intelligence and Statistics, 1995, pp. 112–128.

    Google Scholar 

  26. D.-Y. Cho, K.-H. Cho, and B.-T. Zhang, Identification of biochemical networks by S-tree based genetic programming, Bioinformatics 22 (2006), no. 13, 1631–1640.

    Article  Google Scholar 

  27. J. Collado-Vides and R. Hofestädt (eds.), Gene regulations and metabolism - postgenomic computational approaches, MIT Press, 2002.

    Google Scholar 

  28. G.M. Cooper and R.E. Hausman (eds.), The cell: A molecular approach, 4 ed., ASM Press and Sinauer Associates, 2007.

    Google Scholar 

  29. H. de Jong, Modeling and simulation of genetic regulatory systems: A literature review, J Comput Biol 9 (2002), no. 1, 67–103.

    Article  Google Scholar 

  30. H. de Jong, J.-L. Gouzé, C. Hernandez, M. Page, T. Sari, and J. Geiselmann, Qualitative simulation of genetic regulatory networks using piecewise-linear models, Bull Math Biol 66 (2004), no. 2, 301–340.

    Article  MathSciNet  Google Scholar 

  31. H. de Jong and M. Page, Qualitative simulation of large and complex genetic regulatory systems, Proceedings of the 14th European Conference on Artificial Intelligence (W. Horn, ed.), 2000, pp. 141–145.

    Google Scholar 

  32. P. D’Haeseler, Reconstructing gene networks from large scale gene expression data, Ph.D. thesis, University of New Mexico, 2000.

    Google Scholar 

  33. M. Drton and M.D. Perlman, Model selection for gaussian concentration graphs, Biometrika 91 (2004), no. 3, 591–602.

    Article  MathSciNet  MATH  Google Scholar 

  34. R. Edwards and L. Glass, Combinatorial explosion in model gene networks, Chaos 10 (2000), no. 3, 691–704.

    Article  MathSciNet  MATH  Google Scholar 

  35. B. Ermentrout, Simulating, analyzing and animating dynamical systems: A guide to xppaut for researchers and students, 1 ed., Soc. for Industrial & Applied Math., 2002.

    Google Scholar 

  36. N. Friedman, M. Linial, I. Nachman, and D. Pe’er, Using bayesian networks to analyze expression data, J Comput Biol 7 (2000), no. 3-4, 601–620.

    Article  Google Scholar 

  37. N. Friedman, K. Murphy, and S. Russell, Learning the structure of dynamical probabilistic networks, Proceedings of the 14th Annual Conference on Uncertainty in Artificial Intelligence (San Francisco, CA, USA), Morgan Kaufmann Publishers, 1998, pp. 139–147.

    Google Scholar 

  38. J. Gebert and N. Radde, Modelling procaryotic biochemical networks with differential equations, AIP Conference Proceedings, vol. 839, 2006, pp. 526–533.

    Article  Google Scholar 

  39. D.T. Gillespie, Exact stochastic simulation of coupled chemical reactions, J Phys Chem 81 (1977), no. 25, 2340–2361.

    Article  Google Scholar 

  40. L. Glass and S.A. Kauffman, The logical analysis of continuous, non-linear biochemical control networks, J Theor Biol 39 (1973), 103–129.

    Article  Google Scholar 

  41. J.L. Gouze, Positive and negative circuits in dynamical systems, J Biological Systems 6 (1998), no. 21, 11–15.

    Article  MATH  Google Scholar 

  42. J. Guckenheimer and P. Holmes, Nonlinear oscillations, dynamical systems, and bifurcations of vector fields, Springer Series, New York, 1983.

    MATH  Google Scholar 

  43. M. Gustafsson, M. Hörnquist, and A. Lombardi, Constructing and analyzing a large-scale gene-to-gene regulatory network - lasso-constrained inference and biological validation, IEEE Transaction on Computational Biology and Bioinformatics 2 (2005), no. 3, 254–261.

    Article  Google Scholar 

  44. J. Hasty, D. McMillen, F. Isaacs, and J.J. Collins, Computational studies of gene regulatory networks: in numero molecular biology, Nature Review Genetics 2 (2001), no. 4, 268–279.

    Article  Google Scholar 

  45. D. Heckerman, A tutorial on learning with bayesian networks, Technical Report MSR-TR-95-06, Microsoft Research, Redmond, WA, USA, 1995.

    Google Scholar 

  46. D. Heckerman, D. Geiger, and D.M. Chickering, Learning bayesian networks: The combination of knowledge and statistical data, Machine Learning 20 (1995), 197–243.

    MATH  Google Scholar 

  47. D. Heckerman, A. Mamdani, and M. Wellman, Real-world applications of bayesian networks, Communications of the ACM 38 (1995), no. 3, 24–30.

    Article  Google Scholar 

  48. S. Huang, Gene expression profiling, genetic networks, and cellular states: an integrating concept for tumorigenesis and drug discovery, Journal of Molecular Medicine 77 (1999), 469–480.

    Article  Google Scholar 

  49. F. Jacob and J. Monod, Genetic regulatory mechanisms in the synthesis of proteins, J Mol Biol 3 (1961), 318–356.

    Article  Google Scholar 

  50. L. Kaderali, A hierarchical bayesian approach to regression and its application to predicting survival times in cancer, Shaker Verlag, Aachen, 2006.

    MATH  Google Scholar 

  51. L. Kaderali, T. Zander, U. Faigle, J. Wolf, J.L. Schultze, and R. Schrader, Caspar: A hierarchical bayesian approach to predict survival times in cancer from gene expression data, Bioinformatics 22 (2006), no. 12, 1495–1502.

    Article  Google Scholar 

  52. S. Kauffman, Metabolic stability and epigenesis in randomly constructed genetic nets, J Theor Biol 22 (1969), 437–467.

    Article  MathSciNet  Google Scholar 

  53. S. Kikuchi, D. Tominaga, M. Arita, K. Takahashi, and M. Tomita, Dynamic modeling of genetic networks using genetic algorithm and S-systems, Bioinformatics 19 (2003), no. 5, 643–650.

    Article  Google Scholar 

  54. H. Lähdesmäki, I. Shmulevich, and O. Yli-Harja, On learning gene regulatory networks under the boolean network model, Machine Learning 52 (2003), 147–167.

    Article  MATH  Google Scholar 

  55. W. Lam and F. Bacchus, Using causal information and local measures to learn bayesian networks, Proceedings of the 9th Conference on Uncertainty in Artificial Intelligence (Washington, DC, USA), Morgan Kaufmann Publishers, 1993, pp. 243–250.

    Google Scholar 

  56. R. Laubenbacher and B. Stigler, A computational algebra approach to the reverse engineering of gene regulatory networks, J Theor Biol 229 (2004), no. 4, 523–537.

    Article  MathSciNet  Google Scholar 

  57. F. Li, T. Long, Y. Lu, Q. Ouyangm, and C. Tang, The yeast cell-cycle network is robustly designed, Proc. Natl. Acad. Sci. U. S. A 101 (2004), 4781–4786.

    Article  Google Scholar 

  58. S. Liang, S. Fuhrman, and R. Somogyi, Reveal, a general reverse engineering algorithm for inference of genetic network architectures, Pac Symp Biocomput 3 (1998), 18–29.

    Google Scholar 

  59. D. Madigan, J. Garvin, and A. Raftery, Eliciting prior information to enhance the predictive performance of bayesian graphical models, Communications in Statistics: Theory and Methods 24 (1995), 2271–2292.

    Article  MathSciNet  MATH  Google Scholar 

  60. J.M. Mahaffy, D.A. Jorgensen, and R.L. van der Heyden, Oscillations in a model of repression with external control, J Math Biol 30 (1992), 669–691.

    Article  MathSciNet  MATH  Google Scholar 

  61. J.M. Mahaffy and C.V. Pao, Models of genetic control by repression with time delays and spatial effects, J Math Biol 20 (1984), 39–57.

    Article  MathSciNet  MATH  Google Scholar 

  62. L. Mao and H. Resat, Probabilistic representation of gene regulatory networks, Bioinformatics 20 (2004), no. 14, 2258–2269.

    Article  Google Scholar 

  63. A.A. Margolin, I. Nemenman, K. Basso, C. Wiggins, G. Stolovitzky, R. Dalla-Favera, and A. Califano, Aracne: An algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context, BMC Bioinformatics 7 (Suppl 1) (2006), S7.

    Article  Google Scholar 

  64. A.A. Margolin, K. Wang, W.K. Lim, M. Kustagi, I. Nemenman, and A. Califano, Reverse engineering cellular networks, Nature Protocols 1 (2006), 663–672.

    Article  Google Scholar 

  65. H.H. McAdams and A. Arkin, Stochastic mechanisms in gene expression, Proc. Natl. Acad. Sci. U. S. A. 94 (1997), 814–819.

    Article  Google Scholar 

  66. T. Mestl, E. Plahte, and S.W. Omholt, A mathematical framework for describing and analyzing gene regulatory networks, J Theor Biol 176 (1995), no. 2, 291–300.

    Article  MathSciNet  Google Scholar 

  67. K. Murphy and S. Mian, Modelling gene expression data using dynamic bayesian networks, Tech. report, Computer Science Division, University of California, Berkeley, CA, USA, 1999.

    Google Scholar 

  68. I. Nachman, A. Regev, and N. Friedman, Inferring quantitative models of regulatory networks from expression data, Bioinformatics 20 (2004), no. 1, i248–i256.

    Article  Google Scholar 

  69. S. Ott, S. Imoto, and S. Miyano, Finding optimal models for small gene networks, Pac Symp Biocomput 9 (2004), 557–567.

    Google Scholar 

  70. J. Pearl, Causality: Models, reasoning and inference, Cambridge University Press, Cambridge, 2000.

    MATH  Google Scholar 

  71. J. Pearl and T. Verma, A theory of inferred causation, Knowledge Representation and Reasoning: Proceedings of the Second International Conference (New York) (J. Allen, R. Fikes, and E. Sandewal, eds.), Morgan Kaufmann Publishers, 1991, pp. 441–452.

    Google Scholar 

  72. D. Pe’er, Bayesian network analysis of signaling networks: A primer, Science STKE 281 (2005), p 14.

    Google Scholar 

  73. B.-E. Perrin, L. Ralaivola, A. Mazurie, et al., Gene networks inference using dynamic bayesian networks, Bioinformatics 19 Suppl. II (2003), i138–i148.

    Google Scholar 

  74. N. Radde, J. Gebert, and C.V. Forst, Systematic component selection for gene network refinement, Bioinformatics 22 (2006), 2674–2680.

    Article  Google Scholar 

  75. N. Radde and L. Kaderali, Bayesian inference of gene regulatory networks using gene expression time series data, BIRD 2007, LNBI 4414 (2007), 1–15.

    Google Scholar 

  76. R.W. Robinson, Counting labeled acyclic graphs, New Directions in the Theory of Graphs (F. Harary, ed.), Academic Press, New York, 1973, pp. 239–273.

    Google Scholar 

  77. C. Sabatti and G.M. James, Bayesian sparse hidden components analysis for transcription regulation networks, Bioinformatics 22 (2006), no. 6, 739–746.

    Article  Google Scholar 

  78. M. Santillán and M.C. Mackey, Dynamic regulation of the tryptophan operon: A modeling study and comparison with experimental data, Proc. Natl. Acad. Sci. U. S. A. 98 (2001), no. 4, 1364–1369.

    Article  Google Scholar 

  79. M.J. Schilstra and H. Bolouri, Modelling the regulation of gene expression in genetic regulatory networks, Document for NetBuilder, a graphical tool for building logical representations of genetic regulatory networks.

    Google Scholar 

  80. C.E. Shannon and W. Weaver, The mathematical theory of communication, University of Illinios Press, 1963.

    Google Scholar 

  81. I. Shmulevich, E.R. Dougherty, and W. Zhang, From boolean to probabilistic boolean networks as models of genetic regulatory networks, Proceedings of the IEEE 90 (2002), no. 11, 1778–1792.

    Article  Google Scholar 

  82. I. Shmulevich, A. Saarinen, O. Yli-Harja, and J. Astola, Inference of genetic regulatory networks under the best-fit extension paradigm, Proceedings of the IEEE EURASIP Workshop on Nonlinear Signal and Image Proc. (W. Zhang and I. Shmulevich, eds.), 2001.

    Google Scholar 

  83. A. Silvescu and V. Honavar, Temporal boolean network models of genetic networks and their inference from gene expression time series, Complex Systems 13 (1997), no. 1, 54–75.

    MathSciNet  Google Scholar 

  84. P.W.F. Smith and J. Whittaker, Edge exclusion tests for graphical gaussian models, Learning in Graphical Models (M. Jordan, ed.), MIT Press, 1999, pp. 555–574.

    Google Scholar 

  85. P. Smolen, D.A. Baxter and J.H. Byrne, Modeling transcriptional control in gene networks, Bull Math Biol 62 (2000), 247–292.

    Article  Google Scholar 

  86. R.V. Solé, B. Luque, and S.A. Kauffman, Phase transitions in random networks with multiple states, Technical Report 00-02-011, Santa Fe Institute, 2000.

    Google Scholar 

  87. P.T. Spellman, G. Sherlock, M.Q. Zhang, et al., Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization, Mol Biol Cell 9 (1998), 3273–3297.

    Google Scholar 

  88. D. Spiegelhalter, A. Dawid, S. Lauritzen, and R. Cowell, Bayesian analysis in expert systems, Statistical Science 8 (1993), 219–282.

    Article  MathSciNet  MATH  Google Scholar 

  89. P. Sprites, C. Glymour, and R. Scheines, Causation, prediction, and search, Springer Verlag, New York, 1993.

    Google Scholar 

  90. H. Steck and T. Jaakkola, On the dirichlet prior and bayesian regularization, Advances in Neural Information Processing Systems 15 (Cambridge, MA, USA), MIT Press, 2002.

    Google Scholar 

  91. J. Suzuki, A construction of bayesian networks from databases based on an mdl scheme, Proceedings of the 9th Conference on Uncertainty in Artificial Intelligence (Washington, DC, USA), Morgan Kaufmann Publishers, 1993, pp. 266–273.

    Google Scholar 

  92. D. Thieffry and R. Thomas, Qualitative analysis of gene networks, Pac Symp Biocomput 3 (1998), 77–88.

    Google Scholar 

  93. R. Thomas, On the relation between the logical structure of systems and their ability to generate multiple steady states or sustained oscillations, Springer Series in Synergetics 9 (1981), 180–193.

    Google Scholar 

  94. R. Thomas and R. d’Ari, Biological feedback, CRC Press, Boca Raton, FL, USA, 1990.

    MATH  Google Scholar 

  95. R. Thomas, S. Mehrotra, E.T. Papoutsakis, and V. Hatzimanikatis, A model-based optimization framework for the inference on gene regulatory networks from dna array data, Bioinformatics 20 (2004), no. 17, 3221–3235.

    Article  Google Scholar 

  96. R. Thomas, D. Thieffry, and M. Kauffman, Dynamical behaviour of biological regulatory networks – I. biological role of feedback loops and practical use of the concept of the loop-characteristic state, Bull Math Biol 57 (1995), 247–276.

    MATH  Google Scholar 

  97. M. Tomita, Whole-cell simulation: A grand challenge for the 21st century, Trends Biotechnol. 19 (2001), no. 6, 205–210.

    Article  Google Scholar 

  98. E.P. van Someren, B.L.T. Vaes, W.T. Steegenga, A.M. Sijbers, K.J. Dechering, and J.T. Reinders, Least absolute regression network analysis of the murine osteoblast differentiation network, Bioinformatics 22 (2006), no. 4, 477–484.

    Article  Google Scholar 

  99. E.P. van Someren, L.F.A. Wessels, and M.J.T. Reinders, Linear modeling of genetic networks from experimental data, ISMB 2000: Proceedings of the 8th International Conference on Intelligent Systems for Molecular Biology, 2000, pp. 355–366.

    Google Scholar 

  100. E.O. Voit, Computational analysis of biochemical systems, Cambridge University Press, 2000.

    Google Scholar 

  101. E.O. Voit and J. Almeida, Decoupling dynamical systems for pathway identification from metabolic profiles, Bioinformatics 20 (2004), no. 11, 1670–1681.

    Article  Google Scholar 

  102. J. von Neumann, The theory of self-reproducing automata, University of Illinois Press, 1966.

    Google Scholar 

  103. A. Wagner, Circuit topology and the evolution of robustness in two-gene circadian oscillators, Proc. Natl. Acad. Sci. U. S. A. 102 (2005), 11775–11780.

    Article  Google Scholar 

  104. D.C. Weaver, Modeling regulatory networks with weight matrices, Pac Symp Biocomput, 1999, pp. 112–123.

    Google Scholar 

  105. G. Yagil and E. Yagil, On the relation between effector concentration and the rate of induced enzyme synthesis, Biophysical Journal 11 (1971), no. 1, 11–27.

    Article  Google Scholar 

  106. M. Zou and S.D. Conzen, A new dynamic bayesian network (dbn) approach for identifying gene regulatory networks from time course microarray data, Bioinformatics 21 (2005), no. 1, 71–79.

    Article  Google Scholar 

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Authors and Affiliations

  1. Viroquant Research Group Modeling, Bioquant BQ26, University of Heidelberg, Im Neuenheimer Feld 267, 69120, Heidelberg, Germany

    Lars Kaderali

  2. Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Härtelstr. 16-18, 04107, Leipzig, Germany

    Nicole Radde

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  1. Lars Kaderali
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Editors and Affiliations

  1. Department of Neurology Buffalo Neuroimaging Analysis Center The Jacobs Neurological Institute University at Buffalo, The State University of New York, 100 High Street, Buffalo, NY, 14203, USA

    Arpad Kelemen

  2. Centre for Quantifiable Quality of Service in Communication Systems (Q2S), Centre of Excellence Norwegian University of Science and Technology, O.S. Bragstads plass 2E, N-7491, Trondheim, Norway

    Ajith Abraham

  3. School of Information Science and Engineering, Jinan University, Jiwei Road 106, Jinan, 250022, P.R. China

    Yuehui Chen

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Kaderali, L., Radde, N. (2008). Inferring Gene Regulatory Networks from Expression Data. In: Kelemen, A., Abraham, A., Chen, Y. (eds) Computational Intelligence in Bioinformatics. Studies in Computational Intelligence, vol 94. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76803-6_2

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