Weighted ensemble learning of Bayesian network for gene regulatory networks

Neurocomputing

Volumn 150, Issue PB, 2015, Pages 404-416

  Njah, Hasna ^a   Jamoussi, Salma ^a  

^a Faculty of Economics and Management of Sfax   (Tunisia)

Author keywords

Bayesian network; Bayesian network fusion; Cluster analysis; Ensemble learning; Microarray data

Indexed keywords

BAYESIAN NETWORKS; CLUSTER ANALYSIS; GENES; LEARNING ALGORITHMS;

ENSEMBLE CLUSTERING; ENSEMBLE LEARNING; GENE REGULATORY NETWORKS; MICROARRAY DATA; MICROARRAY DATA SETS; MODELING TOOL; OVER-FITTING TRAINING; PRE-PROCESSING STEP;

KNOWLEDGE BASED SYSTEMS;

ARTICLE; BAYESIAN LEARNING; BAYESIAN NETWORK FUSION; CHOW LIU TREE; COMPUTER PROGRAM; CONTROLLED STUDY; GENE REGULATORY NETWORK; MACHINE LEARNING; MAX MIN HILL CLIMBING; SIMPLE STRUCTURE LEARNING; SIMULATED ANNEALING; TABU SEARCH; WEB BROWSER;

EID: 84922623602     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2014.05.078     Document Type: Article

References (75)

1. K. Abu-Hakmeh, Assessing the use of voting methods to improve Bayesian network structure learning, (Master's thesis), School of Chemical & Biomolecular Engineering, December, 2012.
2. S. Aeberhard, D. Coomans, O. de Vel, Comparison of Classifiers in High Dimensional Settings, Technical Report 92-02, Department of Computer Science and Department of Mathematics and Statistics, James Cook University of North Queensland, 1992.
3. Alizadeh H., Minaei-Bidgoli B., Parvin H. Optimizing fuzzy cluster ensemble in string representation. Int. J. Pattern Recognit. Artif. Intell. 2013, 27(2).
4. Auliac C., Frouin V., Gidrol X., d'Alché Buc F. Evolutionary approaches for the reverse-engineering of gene regulatory networks. a study on a biologically realistic dataset. BMC Bioinform. 2008, 9.
5. I.A. Beinlich, H.J. Suermondt, R.M. Chavez, G.F. Cooper, The alarm monitoring system: A case study with two probabilistic inference techniques for belief networks. in: Proceedings of the 2nd European Conference on Artificial Intelligence in Medicine, Springer-Verlag, 1989, pp. 247-256.
6. Bild A.H., Yao G., Chang J.T., Wang Q., Potti A., Chasse D., Joshi M.-B.B., Harpole D., Lancaster J.M., Berchuck A., Olson J.A., Marks J.R., Dressman H.K., West M., Nevins J.R. Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature 2006, 439(January (7074)):353-357.
7. Binder J., Koller D., Russell S., Kanazawa K. Adaptive probabilistic networks with hidden variables. Mach. Learn. 1997, 29(2-3):213-244.
8. Brookes A., Lehväslaiho H., Siegfried M., Boehm J., Yuan Y., Sarkar C., Bork P., Ortigao F. Hgbase. a database of snps and other variations in and around human genes. Nucleic Acids Res. 2000, 28(1):356-360.
9. Calinski T., Harabasz J. A dendrite method for cluster analysis. Commun. Stat.-Simul. Comput. 1974, 3(1):1-27.
10. Camargo A., Azuaje F. Linking gene expression and functional network data in human heart failure. PLoS ONE 2007, 2(12):e1347.
11. Castelo R., Siebes A. Priors on network structures. biasing the search for Bayesian networks. Int. J. Approx. Reason. 2000, 24(1):39-57.
12. D.M. Chickering, Learning Bayesian Networks is np-hard, Technical Report, 1994.
13. Chow C., Liu C. Approximating discrete probability distributions with dependence trees. IEEE Trans. Inf. Theory 1968, 14(May (3)):462-467.
14. Cooper G., Herskovits E. A Bayesian method for the induction of probabilistic networks from data. Mach. Learn. 1992, 9(4):309-347.
15. Cowell R.G., Dawid A.P., Lauritzen S.L., Spiegelhalter D.J. Probabilistic networks and expert systems. Statistics for Engineering and Information Science 1999, Springer-Verlag, New York.
16. Davies D.L., Bouldin D.W. A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. 1979, 1(February (2)):224-227.
17. De Jong H. Modeling and simulation of genetic regulatory systems. a literature review. J. Comput. Biol. 2002, 9:67-103.
18. T.G. Dietterich, Ensemble methods in machine learning. in: Multiple classifier systems, LBCS-1857, Springer, 2000, pp. 1-15.
19. Djebbari A., Quackenbush J. Seeded Bayesian networks. constructing genetic networks from microarray data. BMC Syst. Biol. 2008, 2(1):57.
20. Dunn J.C. A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters. J. Cybern. 1973, 3(3):32-57.
21. Efron B., Tibshirani R.J. An Introduction to the Bootstrap 1993, Chapman & Hall, New York.
22. Ester M., Kriegel H.-P., Sander J., Xu X. A density-based algorithm for discovering clusters in large spatial databases with noise. KDD 1996, 226-231. AAAI Press. E. Simoudis, J. Han, U.M. Fayyad (Eds.).
23. Everitt B. Cluster Analysis 1980, Halsted Press, London, New York. 2nd edition.
24. L. Fang, K. LeFevre, Privacy wizards for social networking sites, in: Proceedings of the 19th International Conference on World Wide Web. WWW '10, ACM, New York, NY, USA, 2010, pp. 351-360.
25. Fisher R.A. The use of multiple measurements in taxonomic problems. Ann. Eugen. 1936, 7(7):179-188.
26. Friedman N., Goldszmidt M., Wyner A.J. Data analysis with Bayesian networks: a bootstrap approach. UAI 1999, 196-205. Morgan Kaufmann. K.B. Laskey, H. Prade (Eds.).
27. Friedman N., Linial M., Nachman I., Pe'er D. Using Bayesian networks to analyze expression data. J. Comput. Biol. 2000, 7(3-4):601-620.
28. Ghaemi R., Sulaiman M.N., Ibrahim H., Mustapha N. A survey: clustering ensembles techniques 2009, 3(2):535-544.
29. Ghosh J., Acharya A. Cluster ensembles. Wiley Interdiscip. Rev.: Data Min. Knowl. Discov. 2011, 1(4):305-315.
30. Glover F. Tabu search Part i. ORSA J. Comput. 1989, 1(June (3)):190-206.
31. M. Halkidi, M. Vazirgiannis, Clustering validity assessment: Finding the optimal partitioning of a data set, 2001.
32. Halkidi M., Vazirgiannis M., Batistakis Y. On clustering validation techniques. J. Intell. Inf. Syst. 2001, 17(2-3):107-145.
33. Hu L., Wang L. Using consensus Bayesian network to model the reactive oxygen species regulatory pathway. PLoS ONE 2013, 8(2):e56832.
34. Janzura M., Nielsen J. A simulated annealing-based method for learning Bayesian networks from statistical data. Int. J. Intell. Syst. 2006, 21(3):335-348.
35. D. Joita, Unsupervised static discretization methods in data mining, in: Conferinta internationala Educatie si creativitate pentru o societate bazata pe cunoastere, 2008.
36. Kauffman S. Metabolic stability and epigenesis in randomly constructed genetic nets. J. Theor. Biol. 1969, 22(3):437-467.
37. Kramer S., Sorenson H. Bayesian parameter estimation. IEEE Trans. Autom. Control 1988, 33:217-222.
38. Lauritzen S., Spiegelhalter D.J. Local computations with probabilities on graphical structures and their application to expert systems (with discussion). J. R. Stat. Soc. Ser. B 1988, 50:157-224.
39. G. Li, X. Hu, X. Shen, X. Chen, Z. Li, A novel unsupervised feature selection method for bioinformatics data sets through feature clustering, in: GrC, IEEE, 2008, pp. 41-47.
40. Lin A., Wang R.T., Ahn S., Park C.C., Smith D.J. A genome-wide map of human genetic interactions inferred from radiation hybrid genotypes. Genome Res. 2010, 20(August (8)):1122-1132.
41. Y. Liu, Z. Li, H. Xiong, X. Gao, J. Wu, Understanding of internal clustering validation measures, in: 2010 IEEE 10th International Conference on Data Mining (ICDM), IEEE, 2010, pp. 911-916.
42. MacQueen J.B. Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability 1967, vol. 1:281-297. University of California Press.
43. Mardia K.V., Kent J.T., Bibby J.M. Multivariate Analysis. Probability and Mathematical Statistics 1979, Academic Press, London, [u.a.].
44. Matzkevich I., Abramson B. The topological fusion of Bayes nets. UAI 1992, 191-198. Morgan Kaufmann. D. Dubois, M.P. Wellman (Eds.).
45. Moon T.K. The expectation-maximization algorithm. IEEE Signal Process. Mag. 1996, 13(November (6)):47-60.
46. E. Mwebaze, J.A. Quinn, Fast committee-based structure learning, in: JMLR Workshop and Conference Proceedings, vol. 6, 2008, pp. 203-214.
47. E. Mwebaze, J.A. Quinn, Fast committee-based structure learning, in: JMLR Workshop and Conference Proceedings, vol. 6, 2010, pp. 203-214.
48. Myllymäki P., Silander T., Tirri H., Uronen P. B-course. a web-based tool for Bayesian and causal data analysis. Int. J. Artif. Intell. Tools 2002, 11(3):369-387.
49. Naldi M., Salcedo-Sanz S., Carro-Calvo L., Laura L., Portilla-Figueras A., Italiano G.F. A traffic-based evolutionary algorithm for network clustering. Appl. Soft Comput. 2013, 13(11):4303-4319.
50. H.T. Nguyen, Réseaux bayésiens et apprentissage ensembliste pour l'étude différentielle de réseaux de régulation génétique (Ph.D. thesis), Université de Nantes, 2012.
51. H. Njah, S. Jamoussi, Weighted committee-based structure learning for microarray data, in: Proceedings of 13th IEEE International Conference on BioInformatics and BioEngineering, September 2013.
52. Nock R., Nielsen F. On weighting clustering. IEEE Trans. Pattern Anal. Mach. Intell. 2006, 28(8):1223-1235.
53. Pavlin G., de Oude P., Maris M., Nunnink J., Hood T. A multi-agent systems approach to distributed Bayesian information fusion. Inf. Fusion 2010, 11(3):267-282.
54. Pearl J. Fusion, propagation, and structuring in belief networks. Artif. Intell. 1986, 29(3):241-288.
55. Pellet J.-P., Elisseeff A. Using Markov blankets for causal structure learning. J. Mach. Learn. Res. 2008, 9(June):1295-1342.
56. Pensa R.G., Leschi C., Besson J., Boulicaut J.-F. Assessment of discretization techniques for relevant pattern discovery from gene expression data. BIOKDD 2004, 24-30. M.J. Zaki, S. Morishita, I. Rigoutsos (Eds.).
57. D. Qian, H. Wei, Z. Hao, L. Juntao, X. Feng, W. Tao, Y. Zhang, Disease gene explorer: Display disease gene dependency by combining Bayesian networks with clustering, in: CSB, IEEE Computer Society, 2004, pp. 574-575.
58. Redner R.A., Walker H.F. Mixture densities, maximum likelihood and the em algorithm. SIAM Rev. 1984, 26(2):195-239.
59. Rousseeuw P. Silhouettes. a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 1987, 20(1):53-65.
60. Sagrado J.D., Moral S. Qualitative combination of Bayesian networks. Int. J. Intell. Syst. 2003, 18:237-249.
61. Schwarz G. Estimating the dimension of a model. Ann. Stat. 1978, 6:461-464.
62. Segal E., Pe'er D., Regev A., Koller D., Friedman N. Learning module networks. J. Mach. Learn. Res. 2005, 6:557-588.
63. Sigillito V.G., Wing S.P., Hutton L.V., Baker K.B. Classification of radar returns from the ionosphere using neural networks. Johns Hopkins APL Techn. Digest 1989, 262-266.
64. Spirtes P., Glymour C., Scheines R. Causation, Prediction, and Search 2000, MIT Press. 2nd edition.
65. Tsamardinos I., Brown L., Aliferis C. The max-min hill-climbing Bayesian network structure learning algorithm. Mach. Learn. 2006, 65(October (1)):31-78.
66. C. Utz, Learning ensembles of Bayesian network structures using random forest techniques (Master's thesis), University of Oklahoma, 2010.
67. Vega-Pons S., Ruiz-Shulcloper J. A survey of clustering ensemble algorithms. Int. J. Pattern Recognit. Artif. Intell. 2011, 25(3):337-372.
68. L. Vendramin, R.J.G.B. Campello, E.R. Hruschka, On the comparison of relative clustering validity criteria, in: SDM, SIAM, 2009, pp. 733-744.
69. J. Wang, X. Peng, M. Li, Y. Pan, Construction and application of dynamic protein interaction network based on time course gene expression data, Proteomics 13 (2013) 301-313.
70. Wong S., Butz C. Constructing the dependency structure of a multiagent probabilistic network. IEEE Trans. Knowl. Data Eng. 2001, 13:395-415.
71. Wu X., Wang J., Cui X., Maianu L., Rhees B., Rosinski J., So W.V., Willi S.M., Osier M.V., Hill H.S., Page G.P., Allison D.B., Martin M., Garvey W.T. The effect of insulin on expression of genes and biochemical pathways in human skeletal muscle. Endocrine 2007, 31(February):5-17.
72. Xie X.L., Beni G. A validity measure for fuzzy clustering. IEEE Trans. Pattern Anal. Mach. Intell. 1991, 13(8):841-847.
73. Yang Z. Privacy-preserving computation of Bayesian networks on vertically partitioned data. IEEE Trans. Knowl. Data Eng. 2006, 18(9):1253-1264.
74. Yue L., Melnyk P., Gaspo R., Wang Z., Nattel S. Molecular mechanisms underlying ionic remodeling in a dog model of atrial fibrillation. Circ. Res. 1999, 84(7):776-784.
75. Zhang Y., Yue K., Yue M., Liu W. An approach for fusing Bayesian networks. J. Inf. Comput. Sci. 2011, 8:194-201.