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Machine Learning and Genetic Regulatory Networks: A Review and a Roadmap

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Foundations of Computational, Intelligence Volume 1

Part of the book series: Studies in Computational Intelligence ((SCI,volume 201))

Abstract

Genetic regulatory networks (GRNs) are causal structures which can be represented as large directed graphs. Their inference is a central problem in bioinformatics. Because of the paucity of available data and high levels of associated noise, machine learning is essential to performing good and tractable inference of the underlying causal structure.

This chapter serves as a review of the GRN field as a whole, as well as a roadmap for researchers new to the field. It describes the relevant theoretical and empirical biochemistry and the different types of GRN inference. It also describes the data that can be used to perform GRN inference. With this biologically-centred material as background, the chapter surveys previous applications of machine learning techniques and computational intelligence to GRN inference. It describes clustering, logical and mathematical formalisms, Bayesian approaches and some combinations. Each of these is shortly explained theoretically, and important examples of previous research using each are highlighted. Finally, the chapter analyses wider statistical problems in the field, and concludes with a summary of the main achievements of previous research as well as some open research questions in the field.

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References

  1. Arthur, D., Vassilvitskii, S.: k-means++: The advantages of careful seeding. Technical Report 2006-13, Stanford University (2006)

    Google Scholar 

  2. Azuaje, F.: Clustering-based approaches to discovering and visualing microarray data patterns. Brief. Bioinf 4(1), 31–42 (2003)

    Google Scholar 

  3. Balagurunathan, Y., et al.: Noise factor analysis for cDNA microarrays. J. Biomed. Optics 9(4), 663–678 (2004)

    Article  Google Scholar 

  4. Baldwin, J.F., Di Tomaso, E.: Inference and learning in fuzzy Bayesian networks. In: FUZZ 2003: The 12th IEEE Int’l Conf. on Fuzzy Sys., vol. 1, pp. 630–635 (May 2003)

    Google Scholar 

  5. Bar-Joseph, Z., et al.: Computational discovery of gene modules and regulatory networks. Nat. Biotech. 21(11), 1337–1342 (2003)

    Article  Google Scholar 

  6. Barabasi, A.-L., Oltvai, Z.N.: Network biology: Understanding the cell’s functional organisation. Nat. Rev. Genetics 5(2), 101–113 (2004)

    Article  Google Scholar 

  7. Ben-Dor, A., et al.: Clustering gene expression patterns. J. Comp. Bio. 6(3/4), 281–297 (1999)

    Article  Google Scholar 

  8. Di Bernardo, D., et al.: Robust identification of large genetic networks. In: Pacific Symp. on Biocomp., pp. 486–497 (2004)

    Google Scholar 

  9. Bonneau, R., et al.: The inferelator: An algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo. Genome Bio. 7(R36) (2006)

    Google Scholar 

  10. Cao, Y., et al.: Reverse engineering of NK boolean network and its extensions — fuzzy logic network (FLN). New Mathematics and Natural Computation 3(1), 68–87 (2007)

    Article  Google Scholar 

  11. Cao, Y.: Fuzzy Logic Network Theory with Applications to Gene Regulatory Sys. PhD thesis, Department of Electrical and Computer Engineering, Duke University (2006)

    Google Scholar 

  12. Cao, Y., et al.: Pombe gene regulatory network inference using the fuzzy logic network. New Mathematics and Natural Computation

    Google Scholar 

  13. Zeke, S., Chan, H., et al.: Bayesian learning of sparse gene regulatory networks. Biosystems 87(5), 299–306 (2007)

    Google Scholar 

  14. Chickering, D.M.: Learning Bayesian networks is NP-Complete. In: Fisher, D., Lenz, H.J. (eds.) Learning from Data: Artificial Intelligence and Statistics, pp. 121–130. Springer, Heidelberg (1996)

    Google Scholar 

  15. Chu, T., et al.: A statistical problem for inference to regulatory structure from associations of gene expression measurements with microarrays. Bioinf 19(9), 1147–1152 (2003)

    Article  Google Scholar 

  16. Cohen, I., et al.: Learning Bayesian network classifiers for facial expression recognition using both labeled and unlabeled data. CVPR 1, 595–601 (2003)

    Google Scholar 

  17. Conant, G.C., Wagner, A.: Convergent evolution of gene circuits. Nat. Genetics 34(3), 264–266 (2003)

    Article  Google Scholar 

  18. Cui, Q., et al.: Characterizing the dynamic connectivity between genes by variable parameter regression and kalman filtering based on temporal gene expression data. Bioinf. 21(8), 1538–1541 (2005)

    Article  Google Scholar 

  19. de Jong, H.: Modeling and simulation of genetic regulatory systems: A literature review. J. Comp. Bio. 9(1), 67–103 (2002)

    Article  Google Scholar 

  20. de Leon, A.R., Carriere, K.C.: A generalized Mahalanobis distance for mixed data. J. Multivariate Analysis 92(1), 174–185 (2005)

    Article  MATH  Google Scholar 

  21. Dempster, A.P., et al.: Maximum likelihood from incomplete data via the EM algorithm. J. the Royal Statistical Society. Series B (Methodological) 39(1), 1–38 (1977)

    MATH  MathSciNet  Google Scholar 

  22. Dennett, D.C.: Real patterns. J. Philosophy 88, 27–51 (1991)

    Article  Google Scholar 

  23. D’haeseleer, P.: Resconstructing Gene Networks from Large Scale Gene Expression Data. PhD thesis, University of New Mexico, Albuquerque, New Mexico (December 2000)

    Google Scholar 

  24. D’haeseleer, P., Fuhrman, S.: Gene network inference using a linear, additive regulation model. Bioinf. (submitted, 1999)

    Google Scholar 

  25. D’haeseleer, P., et al.: Genetic network inference: From co-expression clustering to reverse engineering. Bioinf. 18(8), 707–726 (2000)

    Article  Google Scholar 

  26. Driscoll, M.E., Gardner, T.S.: Identification and control of gene networks in living organisms via supervised and unsupervised learning. J. Process Control 16(3), 303–311 (2006)

    Article  Google Scholar 

  27. Eisen, M.B., et al.: Cluster analysis and display of genome-wide expression patterns. Proc. of the National Academy of Sciences USA 95(25), 14863–14868 (1998)

    Article  Google Scholar 

  28. FitzGerald, P.C., et al.: Comparative genomics of drosophila and human core promoters. Genome Bio. 7, R53+ (2006)

    Google Scholar 

  29. Floyd, R.W.: Algorithm 97: Shortest path. Communications of the ACM 5(6), 345 (1962)

    Article  Google Scholar 

  30. Fogelberg, C.: Belief propagation in fuzzy Bayesian networks: A worked example. In: Faily, S., Zivny, S. (eds.) Proc. 2008 Comlab. Student Conference (October 2008)

    Google Scholar 

  31. Fogelberg, C., Palade, V.: GreenSim: A genetic regulatory network simulator. Tech. Report PRG-RR-08-07, Computing Laboratory, Oxford University, Wolfson Building, Parks Road, Oxford, OX1-3QD (May 2008), http://syntilect.com/cgf/pubs:greensimtr

  32. Fogelberg, C., Zhang, M.: Linear genetic programming for multi-class object classification. In: Zhang, S., Jarvis, R. (eds.) AI 2005. LNCS, vol. 3809, pp. 369–379. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  33. Fogelberg, C., et al.: Belief propagation in fuzzy bayesian networks. In: Hatzilygeroudis, I. (ed.) 1st Int’l Workshop on Combinations of Intelligent Methods and Applications(CIMA) at ECAI 2008, University of Patras, Greece, July 21–22 (2008)

    Google Scholar 

  34. Friedman, N.: Learning belief networks in the presence of missing values and hidden variables. In: Proc. of the 14th Int’l Conf. on Machine Learning, pp. 125–133. Morgan Kaufmann, San Francisco (1997)

    Google Scholar 

  35. Friedman, N., et al.: Learning the structure of dynamic probabilistic networks. In: Proc. of the 14th Annual Conf. on Uncertainty in Artificial Intelligence (UAI 1998), vol, pp. 139–147. Morgan Kaufmann, San Francisco (1998)

    Google Scholar 

  36. Friedman, N., et al.: Using Bayesian networks to analyze expression data. J. Comp. Bio. 7(3), 601–620 (2000)

    Article  Google Scholar 

  37. Gardner, T.S., et al.: Inferring microbial genetic networks. ASM News 70(3), 121–126 (2004)

    Google Scholar 

  38. Geman, S., Geman, D.: Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence 6, 721–742 (1984)

    Article  MATH  Google Scholar 

  39. Giaever, G., et al.: Functional profiling of the Saccharomyces cerevisiae genome. Nat. 418(6896), 387–391 (2002)

    Article  Google Scholar 

  40. Grünwald, P.: The minimum description length principle and non-deductive inference. In: Flach, P. (ed.) Proc. of the IJCAI Workshop on Abduction and Induction in AI, Japan (1997)

    Google Scholar 

  41. Guo, H., Hsu, W.: A survey of algorithms for real-time Bayesian network inference. In: Joint AAAI 2002/KDD 2002/UAI 2002 workshop on Real-Time Decision Support and Diagnosis Sys. (2002)

    Google Scholar 

  42. Gurney, K.: An Introduction to Neural Networks. Taylor & Francis, Inc., Bristol (1997)

    Google Scholar 

  43. Han, E.-H., et al.: Clustering based on association rule hypergraphs. In: Research Issues on Data Mining and Knowledge Discovery, TODO (1997)

    Google Scholar 

  44. Harbison, C.T., et al.: Transcriptional regulatory code of a eukaryotic genome. Nat 431(7004), 99–104 (2004)

    Article  Google Scholar 

  45. Hartemink, A.J., et al.: Combining location and expression data for principled discovery of genetic regulatory network models. In: Pacific Symp. on Biocomp, pp. 437–449 (2002)

    Google Scholar 

  46. Hastings, W.K.: Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57(1), 97–109 (1970)

    Article  MATH  Google Scholar 

  47. Heckerman, D.: A tutorial on learning with Bayesian networks. Technical report, Microsoft Research, Redmond, Washington (1995)

    Google Scholar 

  48. Heng, X.-C., Qin, Z.: Fpbn: A new formalism for evaluating hybrid Bayesian networks using fuzzy sets and partial least-squares. In: Huang, D.-S., Zhang, X.-P., Huang, G.-B. (eds.) ICIC 2005. LNCS, vol. 3645, pp. 209–217. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  49. Herrgard, M.J., et al.: Reconciling gene expression data with known genome-scale regulatory network structures. Genome Research 13(11), 2423–2434 (2003)

    Article  Google Scholar 

  50. Hinman, V.F., et al.: Developmental gene regulatory network architecture across 500 million years of echinoderm evolution. Proc. of the National Academcy of Sciences, USA 100(23), 13356–13361 (2003)

    Article  Google Scholar 

  51. Horimoto, K., Toh, H.: Statistical estimation of cluster boundaries in gene expression profile data. Bioinf. 17(12), 1143–1151 (2001)

    Article  Google Scholar 

  52. Imoto, S., et al.: Estimation of genetic networks and functional structures between genes by using Bayesian networks and nonparametric regression. In: Pacific Symp. on Biocomp., vol. 7, pp. 175–186 (2002)

    Google Scholar 

  53. Imoto, S., et al.: Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network. J. Bioinf. and Comp. Bio. 1(2), 231–252 (2003)

    Article  Google Scholar 

  54. Jarvis, E.D., et al.: A framework for integrating the songbird brain. J. Comp. Physiology A 188, 961–980 (2002)

    Article  Google Scholar 

  55. Jiang, D., et al.: Cluster analysis for gene expression data: A survey. IEEE Transactions on Knowledge and Data Engineering 16(11), 1370–1386 (2004)

    Article  Google Scholar 

  56. Kauffman, S.A.: The Origins of Order: Self-Organization and Selection in Evolution. Oxford University Press, Oxford (1993)

    Google Scholar 

  57. Kauffman, S.A.: Antichaos and adaptation. Scientific American 265(2), 78–84 (1991)

    Article  MathSciNet  Google Scholar 

  58. Kim, S., et al.: Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data. Biosys 75(1-3), 57–65 (2004)

    Article  Google Scholar 

  59. Kitano, H.: Computational systems biology. Nat 420(6912), 206–210 (2002)

    Article  Google Scholar 

  60. Klebanov, L., Yakovlev, A.: How high is the level of technical noise in microarray data? Bio. Direct 2, 9+ (2007)

    Google Scholar 

  61. Koch, M.A., et al.: Comparative genomics and regulatory evolution: conservation and function of the chs and apetala3 promoters. Mol. Bio. and Evolution 18(10), 1882–1891 (2001)

    Google Scholar 

  62. Krause, E.F.: Taxicab Geometry. Dover Publications (1987)

    Google Scholar 

  63. Kyoda, K.M., et al.: A gene network inference method from continuous-value gene expression data of wild-type and mutants. Genome Informatics 11, 196–204 (2000)

    Google Scholar 

  64. Lähdesmäki, H., et al.: On learning gene regulatory networks under the Boolean network model. Machine Learning 52(1–2), 147–167 (2003)

    Article  MATH  Google Scholar 

  65. Lam, W., Bacchus, F.: Learning Bayesian belief networks: An approach based on the MDL principle. In: Comp. Intelligence, vol. 10, pp. 269–293 (1994)

    Google Scholar 

  66. Laplace, P.-S.: Essai philosophique sur les probabilités. Mme. Ve. Courcier (1814)

    Google Scholar 

  67. Le, P.P., et al.: Using prior knowledge to improve genetic network reconstruction from microarray data. Silico Bio. 4 (2004)

    Google Scholar 

  68. Liang, S., et al.: REVEAL: a general reverse enginerring algorithm for inference of genetic network architectures. In: Pacific Symp. on Biocomp, pp. 18–29 (1998)

    Google Scholar 

  69. Lum, P.Y., et al.: Discovering modes of action for therapeutic compounds using a genome-wide screen of yeast heterozygotes. Cell 116(1), 121–137 (2004)

    Article  Google Scholar 

  70. MacKay, D.J.C.: Introduction to Monte Carlo methods. In: Jordan, M.I. (ed.) Learning in Graphical Models. NATO Science Series, pp. 175–204. Kluwer, Dordrecht (1998)

    Google Scholar 

  71. MacKay, D.J.C.: Information Theory, Inference, and Learning Algorithms. Cambridge University Press, Cambridge (2003)

    Google Scholar 

  72. MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Proc. of the 5th Berkeley Symp. on Mathematical Statistics and Probability, pp. 281–297. University of California Press (1967)

    Google Scholar 

  73. Madeira, S.C., Oliveira, A.L.: Biclustering algorithms for biological data analysis: a survey. IEEE/ACM Transactions on Comp. Bio. and Bioinf. 1(1), 24–45 (2004)

    Article  Google Scholar 

  74. Mahalanobis, P.C.: On the generalised distance in statistics. Proc. of the National Institute of Science of India 12, 49–55 (1936)

    Google Scholar 

  75. Marnellos, G., Mjolsness, E.: A gene network approach to modeling early neurogenesis in drosophila. In: Pacific Symp. on Biocomp., vol. 3, pp. 30–41 (1998)

    Google Scholar 

  76. Massimo, F., Mascioli, F., et al.: Scale-based approach to hierarchical fuzzy clustering. Signal Processing 80(6), 1001–1016 (2000)

    Article  Google Scholar 

  77. McShan, D.C., et al.: Symbolic inference of xenobiotic metabolism. In: Altman, R.B., et al. (eds.) Pacific Symp. on Biocomp., pp. 545–556. World Scientific, Singapore (2004)

    Google Scholar 

  78. Metropolis, N.A., et al.: Equation of state calculations by fast computing machines. J. Chemical Physics 21, 1087–1092 (1956)

    Article  Google Scholar 

  79. Mjolsness, E., et al.: Multi-parent clustering algorithms from stochastic grammar data models. Technical Report JPL-ICTR-99-5, JPL (1999)

    Google Scholar 

  80. Motsinger, A.A., et al.: GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease. BMC Bioinf. 7, 39 (2006)

    Google Scholar 

  81. Murali, T.M., Kasif, S.: Extracting conserved gene expression motifs from gene expression data. In: Pacific Symp. on Biocomp., pp. 77–88 (2003)

    Google Scholar 

  82. Murphy, K.: Learning Bayes net structure from sparse data sets. Technical report, Comp. Sci. Div., UC Berkeley (2001)

    Google Scholar 

  83. Murphy, K., Mian, S.: Modelling gene expression data using dynamic Bayesian networks. Technical report, Computer Science Division, University of California, Berkeley, CA (1999)

    Google Scholar 

  84. Neal, R.M.: Probabilistic inference using Markov chain Monte Carlo methods. Technical Report CRG-TR-93-1, University of Toronto (1993)

    Google Scholar 

  85. Nykter, M., et al.: Simulation of microarray data with realistic characteristics. Bioinf. 7, 349 (2006)

    Google Scholar 

  86. Pan, H., Liu, L.: Fuzzy Bayesian networks - a general formalism for representation, inference and learning with hybrid Bayesian networks. IJPRAI 14(7), 941–962 (2000)

    Google Scholar 

  87. Pan, H., McMichael, D.: Fuzzy causal probabilistic networks - a new ideal and practical inference engine. In: Proc. of the 1st Int’l Conf. on Multisource-Multisensor Information Fusion (July 1998)

    Google Scholar 

  88. Park, H.-S., et al.: A context-aware music recommendation system using fuzzy Bayesian networks with utility theory. In: Wang, L., Jiao, L., Shi, G., Li, X., Liu, J. (eds.) FSKD 2006. LNCS, vol. 4223, pp. 970–979. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  89. Pearl, J.: Causal diagrams for empirical research. Biometrika 82(4), 669–709 (1995)

    Article  MATH  MathSciNet  Google Scholar 

  90. Perkins, T.J., et al.: Reverse engineering the gap gene network of drosophila melanogaster. PLoS Comp. Bio. 2(5), e51+ (2006)

    Google Scholar 

  91. Pritsker, M., et al.: Whole-genome discovery of transcription factor binding sites by network-level conservation. Genome Research 14(1), 99–108 (2004)

    Article  Google Scholar 

  92. Ranawana, R., Palade, V.: Multi-classifier systems: Review and a roadmap for developers. Int’l J. Hybrid Intelligent Sys. 3(1), 35–61 (2006)

    MATH  Google Scholar 

  93. Ritchie, M.D., et al.: Optimization of neural network architecture using genetic programming improves detection and modeling of gene-gene interactions in studies of human diseases. BMC Bioinf. 4, 28 (2003)

    Google Scholar 

  94. Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, 2nd edn. Prentice Hall, Englewood Cliffs (2002)

    Google Scholar 

  95. Schwarz, G.: Estimating the dimension of a model. The Annals of Statistics 6(2), 461–464 (1978)

    Article  MATH  MathSciNet  Google Scholar 

  96. Segal, E., et al.: Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat. Genetics 34(2), 166–176 (2003)

    Article  Google Scholar 

  97. Segal, E., et al.: From signatures to models: Understanding cancer using microarrays. Nat. Genetics 37, S38–S45 (2005) (By invitation)

    Google Scholar 

  98. Shamir, R., Sharan, R.: Algorithmic approaches to clustering gene expression data. In: Jiang, T., Smith, T., Xu, Y., Zhang, M.Q. (eds.) Current Topics in Comp. Bio., pp. 269–300. MIT press, Cambridge (2002)

    Google Scholar 

  99. Shannon, C.E.: A mathematical theory of communication. Bell System Technical Journal 27, 379–423, 623–656 (1948)

    Google Scholar 

  100. Sheng, Q., et al.: Biclustering microarray data by Gibbs sampling. Bioinf. 19, ii196–ii205 (2003)

    Google Scholar 

  101. Silvescu, A., Honavar, V.: Temporal Boolean network models of genetic networks and their inference from gene expression time series. Complex Sys 13, 54–70 (2001)

    MathSciNet  Google Scholar 

  102. Sivia, D.S.: Data Analysis: A Bayesian Tutorial. Clarendon Press, Oxford (1996)

    Google Scholar 

  103. Smith, V.A., et al.: Evaluating functional network inference using simulations of complex biological systems. Bioinf. 18, S216–S224 (2002)

    Google Scholar 

  104. Smith, V.A., et al.: Influence of network topology and data collection on network inference. In: Pacific Symp. on Biocomp., pp. 164–175 (2003)

    Google Scholar 

  105. Spellman, P.T., et al.: Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol. Bio. of the Cell 9(12), 3273–3297 (1998)

    Google Scholar 

  106. Spirtes, P., et al.: Constructing Bayesian network models of gene expression networks from microarray data. In: Proc. of the Atlantic Symp. on Comp. Bio., Genome Information Sys. and Technology (2000)

    Google Scholar 

  107. Sterelny, K., Griffiths, P.E.: Sex and Death: An Introduction to Philosophy of Bio. Science and Its Conceptual Foundations series. University Of Chicago Press (June 1999) ISBN 0226773043

    Google Scholar 

  108. Tang, C., et al.: Interrelated two-way clustering: An unsupervised approach for gene expression data analysis. In: Proc. of the IEEE 2nd Int’l Symp. on Bioinf. and Bioeng. Conf., 2001, November 4–6, pp. 41–48 (2001)

    Google Scholar 

  109. Tegner, J., et al.: Reverse engineering gene networks: integrating genetic perturbations with dynamical modeling. Proc. of the National Academy of Sciences, USA 100(10), 5944–5949 (2003)

    Article  Google Scholar 

  110. Thomas, R.: Laws for the dynamics of regulatory networks. Int’l J. Developmental Bio. 42, 479–485 (1998)

    Google Scholar 

  111. Tibshirani, R., et al.: Clustering methods for the analysis of DNA microarray data. Technical report, Stanford University (October 1999)

    Google Scholar 

  112. Toh, H., Horimoto, K.: Inference of a genetic network by a combined approach of cluster analysis and graphical Gaussian modeling. Bioinf. 18(2), 287–297 (2002)

    Article  Google Scholar 

  113. Tong, A.H., et al.: Systematic genetic analysis with ordered arrays of yeast deletion mutants. Science 294(5550), 2364–2368 (2001)

    Article  Google Scholar 

  114. Troyanskaya, O., et al.: Missing value estimation methods for DNA microarrays. Bioinf. 17(6), 520–525 (2001)

    Article  Google Scholar 

  115. Vert, J.-P., Yamanishi, Y.: Supervised graph inference. In: Saul, L.K., et al. (eds.) Advances in Neural Information Processing Sys., vol. 17, pp. 1433–1440. MIT Press, Cambridge (2005)

    Google Scholar 

  116. Vohradskỳ, J.: Neural network model of gene expression. FASEB Journal 15, 846–854 (2001)

    Article  Google Scholar 

  117. Wang, Y., et al.: Inferring gene regulatory networks from multiple microarray datasets. Bioinf. 22(19), 2413–2420 (2006)

    Article  Google Scholar 

  118. Xu, R., Wunsch II, D.: Survey of clustering algorithms. IEEE Transactions on Neural Networks 16(3), 645–678 (2005)

    Article  Google Scholar 

  119. Yamanishi, Y., et al.: Protein network inference from multiple genomic data: a supervised approach. Bioinf. 20(1), 363–370 (2004)

    Article  Google Scholar 

  120. Yang, E., et al.: A novel non-overlapping bi-clustering algorithm for network generation using living cell array data. Bioinf. 23(17), 2306–2313 (2007)

    Article  Google Scholar 

  121. Yu, J., et al.: Using Bayesian network inference algorithms to recover molecular genetic regulatory networks. In: Int’l Conf. on Sys. Bio. (ICSB 2002) (December 2002)

    Google Scholar 

  122. Yu, J., et al.: Advances to Bayesian network inference for generating causal networks from observational biological data. Bioinf. 20(18), 3594–3603 (2004)

    Article  Google Scholar 

  123. Yuh, C.H., et al.: Genomic cis-regulatory logic: Experimental and computational analysis of a sea urchin gene. Science 279, 1896–1902 (1998)

    Article  Google Scholar 

  124. Zhang, Y., et al.: Dynamic Bayesian network (DBN) with structure expectation maximization (SEM) for modeling of gene network from time series gene expression data. In: Arabnia, H.R., Valafar, H. (eds.) BIOCOMP, pp. 41–47. CSREA Press (2006)

    Google Scholar 

  125. Zhou, X., et al.: Gene clustering based on clusterwide mutual information. J. Comp. Bio. 11(1), 147–161 (2004)

    Article  Google Scholar 

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Authors and Affiliations

  1. Oxford University Computing Laboratory, Wolfson Building, OX1-3QD, UK

    Christopher Fogelberg & Vasile Palade

  2. Oxford-Man Institute, OX1-4EH, UK

    Christopher Fogelberg

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  1. College of Business Administration, Quantitative and Information System Department, Kuwait University, P.O. Box 5486, 13055, Safat, Kuwait

    Aboul-Ella Hassanien

  2. Center of Excellence for Quantifiable, Quality of Service, Norwegian University of Science & Technology, O.S. Bragstads plass 2E, 7491, Trondheim, Norway

    Ajith Abraham

  3. Department of Computer and Telecommunications Engineering, University ofWestern Macedonia, Agios Dimitrios Park, 50 100, Kozani, Greece

    Athanasios V. Vasilakos

  4. Dept. Electrical and Computer Engineering, University of Alberta, T6J 2V4, Edmonton,Alberta, Canada

    Witold Pedrycz

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Fogelberg, C., Palade, V. (2009). Machine Learning and Genetic Regulatory Networks: A Review and a Roadmap. In: Hassanien, AE., Abraham, A., Vasilakos, A.V., Pedrycz, W. (eds) Foundations of Computational, Intelligence Volume 1. Studies in Computational Intelligence, vol 201. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01082-8_1

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