Bayesian networks classifiers for gene-expression data

International Conference on Intelligent Systems Design and Applications, ISDA

Volumn , Issue , 2011, Pages 1200-1206

  De Campos, Luis M ^a   Cano, Andrés ^a   Castellano, Javier G ^a   Moral, Serafín ^a  

^a UNIVERSITY OF GRANADA   (Spain)

Author keywords

BAN; Bayesian classifier; Bayesian multinet; Bayesian network; C RPDAG; DNA microarray; gene expression data; Machine learning; na ve Bayes; Selective na ve Bayes; supervised classification; TAN

Indexed keywords

BAN; BAYESIAN CLASSIFIER; BAYESIAN MULTINET; C-RPDAG; DNA MICROARRAY; GENE EXPRESSION DATA; MACHINE-LEARNING; SUPERVISED CLASSIFICATION; TAN;

BAYESIAN NETWORKS; INTELLIGENT SYSTEMS; SYSTEMS ANALYSIS;

GENE EXPRESSION;

EID: 84857580325     PISSN: 21647143     EISSN: 21647151     Source Type: Conference Proceeding    
DOI: 10.1109/ISDA.2011.6121822     Document Type: Conference Paper

References (68)

1. R. Duda and P. Hart, Pattern classification and scene analysis. John Wiley and Sons, New York, 1973.
2. P. Langley, W. Iba, and K. Thompson, "An analysis of Bayesian classifiers," in National Conference on Artificial Intelligence, 1992, pp. 223-228.
3. N. Friedman, D. Geiger, and M. Goldszmidt, "Bayesian networks classifiers," Machine Learning, vol. 29, pp. 131-163, 1997.
4. J. Cheng and R. Greiner, "Comparing Bayesian network classifiers," in UAI '99: Proc. of the Fifteenth Conference on Uncertainty in Artificial Intelligence, 1999, pp. 101-108.
5. -, "Learning Bayesian belief network classifiers: Algorithms and system," in AI '01: Proc. of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence. Springer-Verlag, 2001, pp. 141-151.
6. M. Sahami, "Learning limited dependence Bayesian classifiers,"Second International Conference on Knowledge Discovery in Databases, pp. 335-338, 1996.
7. I. Kononenko, "Semi-naive Bayesian classifier." in European Working Session on Learning on Machine Learning, 1991, pp. 206-219.
8. M. J. Pazzani, "Searching for dependencies in Bayesian classifiers," in Artificial Intelligence and Statistics IV, ser. Lecture Notes in Statistics. Springer-Verlag, 1997.
9. P. Langely and S. Sage, "Induction of selective Bayesian classifiers,"Proceeding of the Tenth Conference on Uncertainty in Artificial Intelligence, pp. 399-406, 1998.
10. G. H. John, R. Kohavi, and K. Pfleger, "Irrelevant features and the subset selection problem," in International Conference on Machine Learning, 1994, pp. 121-129.
11. S. Acid, L. M. de Campos, and J. G. Castellano, "Learning Bayesian network classifiers: Searching in a space of partially directed acyclic graphs," Machine Learning, vol. 59, no. 3, pp. 213-235, 2005. (Pubitemid 40890478)
12. A. Cano, J. G. Castellano, A. R. Masegosa, and S. Moral, "Methods to determine the branching attribute in bayesian multinets classifiers," in Symbolic and Quantitative Approaches to Reasoning with Uncertainty ECSQARU2005, vol. 3571, 2005, pp. 932-943.
13. M. Schena, D. Shalon, R. W. Davis, and P. O. Brown, "Quantitative monitoring of gene expression patterns with a complementary DNA microarray." Science, vol. 270, no. 5235, pp. 467-70, 1995.
14. P. Spirtes et al., "Constructing Bayesian network models of gene expression networks from microarray data," in Proc. of the Atlantic Symposium on Computational Biology, Genome Information Systems and Technology, 2000.
15. I. Inza, B. Sierra, R. Blanco, and P. Larrañaga, "Gene selection by sequential search wrapper approaches in microarray cancer class prediction." Journal of Intelligent and Fuzzy Systems, vol. 12, no. 1, pp. 25-34, 2002.
16. I. Inza, P. Larrañaga, R. Blanco, and A. J. Cerrolaza, "Filter versus wrapper gene selection approaches in DNA microarray domains." Artificial Intelligence in Medicine, vol. 31, no. 2, pp. 91-103, 2004. (Pubitemid 38823034)
17. R. Blanco, P. Larrañaga, I. Inza, and B. Sierra, "Selection of highly accurate genes for cancer classification by estimation of distribution algorithms," in Proc. of the Workshop Bayesian Models in Medicine, held within AIME 2001, Artificial Intelligence in Medicine, 2001, pp. 29-34.
18. P. Larrañaga and J. A. Lozano, Estimation of distribution algorithms: A new tool for evolutionary computation, ser. Genetic Algorithms and Evolutionary Computation. Kluwer Academic Publishers, 2001.
19. A. Ben-Dor, N. Friedman, and Z. Yakhini, "Scoring genes for relevance," School of Computer Science & Engineering, Hebrew University, Jerusalem, Tech. Rep. 2000-38, 2000.
20. -, "Class discovery in gene expression data," in RECOMB '01: Proc. of the fifth annual international conference on Computational biology. ACM Press, 2001, pp. 31-38.
21. M. Mramor, G. Leban, J. Demsar, and B. Zupan, "Visualization-based cancer microarray data classification analysis," Bioinformatics, 2007.
22. A. Osareh and B. Shadgar, "Classification and diagnostic prediction of cancers using gene microarray data analysis,"Journal of Applied Sciences, vol. 9, no. 3, pp. 459-468, 2009.
23. E. Moler, M. Chow, and I. Mian, "Analysis of molecular profile data using generative and discriminative methods."Physiological Genomics, vol. 4, no. 2, pp. 109-126, 2000.
24. L. Fan, K.-L. Poha, and P. Zhoub, "A sequential feature extraction approach for naïve bayes classification of microarray data," Expert Systems with Applications, vol. 36, no. 6, pp. 9919-9923, 2009.
25. A. Cano, J. G. Castellano, A. R. Masegosa, and S. Moral, "Application of a selective Gaussian naive bayes model for diffuse large-B-cell lymphoma classification." in Proc. of the 2nd European Workshop on Probabilistic Graphical Models(PGM'04), 2004, pp. 33-40.
26. -, "Selective gaussian naive bayes model for diffuse large-B-cell lymphoma classification: Some improvements in preprocessing and variable elimination," in Symbolic and Quantitative Approaches to Reasoning with Uncertainty ECSQARU2005, vol. 3571, 2005, pp. 908-920.
27. R. Armañanzas, I. Inza, and P. Larrañaga, "Detecting reliable gene interactions by a hierarchy of Bayesian network classifiers,"Computer Methods and Programs in Biomedicine, vol. 91, no. 2, pp. 110-121, 2008.
28. M. A. Hall and L. A. Smith, "Feature subset selection: a correlation based filter approach," in International Conference on Neural Information Processing and Intelligent Information Systems. Springer, 1997, pp. 855-858.
29. R. Armañanzas et al., "Bayesian classifiers with consensus gene selection: A case study in the systemic lupus erythematosus,"in Progress in Industrial Mathematics at ECMI 2006, 2007, vol. 12, pp. 560-565.
30. A. Bosin, N. Dessì, D. Liberati, and B. Pes, "Learning Bayesian classifiers from gene-expression microarray data,"in Fuzzy Logic and Applications, 6th International Workshop, WILF 2005, vol. 3849, 2005, pp. 297-304.
31. K. B. Hwang et al., "Applying machine learning techniques to analysis of gene expression data: Cancer diagnosis," in Methods of Microarray Data Analysis (Proc. of CAMDA'00),, 2002, pp. 167-182.
32. B. T. Zhang and K. B. Hwang, "Bayesian network classifiers for gene expression analysis," in A Practical Approach to Microarray Data Analysis, 2003, pp. 150-165.
33. J. H. Chang, K. B. Hwang, and B. T. Zhang, "Analysis of gene expression profiles and drug activity patterns by clustering and Bayesian network learning," in Methods of Microarray Data Analysis II (Proc. of CAMDA'01),, 2002, pp. 169-184.
34. J. Bockhorst et al., "A Bayesian network approach to operon prediction." Bioinformatics, vol. 19, no. 10, pp. 1227-35, 2003. (Pubitemid 36850215)
35. M. Craven et al., "A probabilistic learning approach to wholegenome operon prediction." in Proc. of the 8th International Conference on Intelligent Systems for Molecular Biology, vol. 8, 2000, pp. 116-27.
36. Q. Diao et al., "Disease gene explorer: Display disease gene dependency by combining Bayesian networks with clustering,"in 3rd International IEEE Computer Society Computational Systems Bioinformatics Conference, 2004, pp. 574-575.
37. O. Gevaert et al., "Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks," Bioinformatics, vol. 22, no. 14, pp. e184-e190, 2006. (Pubitemid 44288285)
38. O. Gevaert, S. Van Vooren, and B. de Moor, "Integration of microarray and textual data improves the prognosis prediction of breast, lung and ovarian cancer patients." Pacific Symposium on Biocomputing, pp. 279-290, 2008.
39. K. B. Hwang and B. T. Zhang, "Bayesian model averaging of Bayesian network classifiers over multiple node-orders: application to sparse datasets," IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 35, no. 6, pp. 1302-1310, 2005.
40. A. Tucker, V. Vinciotti, P. A. C. 't Hoen, and X. Liu, "Bayesian network classifiers for time-series microarray data," in Advances in Intelligent Data Analysis VI, 6th International Symposium on Intelligent Data Analysis, IDA 2005, Proc., vol. 3646. Springer, 2005, pp. 475-485.
41. I. Sen et al., "Context-specific gene regulations in cancer gene expression data," in Pacific Symposium on Biocomputing, vol. 14, 2009, pp. 75-86.
42. J. Li, H. Liu, and L. Wong, "Mean-entropy discretized features are effective for classifying high-dimensional biomedical data," in Proc. of the 3nd ACM SIGKDD Workshop on Data Mining in Bioinformatics, 2003, pp. 17-24.
43. L. J. van 't Veer et al., "Gene expression profiling predicts clinical outcome of breast cancer." Nature, vol. 415, no. 6871, pp. 530-536, 2002.
44. S. L. Pomeroy et al., "Prediction of central nervous system embryonal tumour outcome based on gene expression," Nature, vol. 415, pp. 436 - 42, 2002.
45. U. Alon et al., "Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays," Proc. of the National Academy of Sciences of the United States of America, vol. 96, no. 12, pp. 6745-6750, 1999. (Pubitemid 29274954)
46. A. Alizadeh et al., "Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling," Nature, vol. 403, pp. 503-511, 2000. (Pubitemid 30082188)
47. M. A. Shipp et al., "Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning," Nature Medicine, vol. 8, pp. 68-74, 2002.
48. T. R. Golub et al., "Molecular classification of cancer: class discovery and class prediction by gene expression monitoring,"Science, vol. 286, pp. 531-537, 1999.
49. S. A. Armstrong et al., "MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia,"Nature Genetics, vol. 30, pp. 41-7, 2002.
50. A. Bhattacharjee et al., "Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses," Proc. of the National Academy of Sciences of the United States of America, vol. 98, pp. 13790-5, 2001. (Pubitemid 33115977)
51. D. Singh et al., "Gene expression correlates of clinical prostate cancer behavior," Cancer Cell, vol. 1, pp. 203-9, 2002. (Pubitemid 41039166)
52. R. Blanco, "Learning Bayesian networks from data with factorisation and classification purposes. applications in biomedicine." Ph.D. dissertation, University of the Basque Country, Spain, 2005.
53. A. R. Masegosa, "Model of supervised classification: Applications to genomics and information retrieval." Ph.D. dissertation, Granada University, 2009.
54. A. Cano, J. G. Castellano, A. Masegosa, and S. Moral, "Aplicación de un modelo naïve bayes gaussiano con selección de variables al análisis de datos de expresión genética," in Actas de las VI Jornadas de Transferencia Tecnológica de Inteligencia Artificial (TTIA'2005)., 2005, pp. 81-88.
55. H.-H. Chang and M. Ramoni, "Transcriptional network classifiers,"BMC Bioinformatics, vol. 10, no. 9, p. S1, 2009.
56. W. Stacklies et al., "pcaMethods-a bioconductor package providing PCA methods for incomplete data," Bioinformatics, vol. 23, no. 9, pp. 1164-1167, 2007. (Pubitemid 47050571)
57. E. Wit and J. Mcclure, Statistics for microarrays. John Wiley & Sons, 2004.
58. K. Sachs et al., "Causal protein-signaling networks derived from multiparameter single-cell data," Science, vol. 308, no. 5721, pp. 523-529, 2005. (Pubitemid 40570578)
59. M. Bansal, V. Belcastro, A. Ambesi-Impiombato, and D. di Bernardo, "How to infer gene networks from expression profiles," Molecular Systems Biology, vol. 3, 2007.
60. S. A. Kauffman, "Metabolic stability and epigenesis in randomly constructed genetic nets," Journal of Theoretical Biology, vol. 22, no. 3, pp. 437-467, 1969.
61. N. Friedman, M. Linial, I. Nachman, and D. Pe'er, "Using Bayesian networks to analyze expression data." Journal of Computational Biology, vol. 7, no. 3-4, pp. 601-620, 2000.
62. U. Fayyad and K. Irani, "Multi-valued interval discretization of continuous-valued attributes for classification learning,"in Proceeding of the 13th International joint Conference on Artificial Inteligence, 1993, pp. 1022-1027.
63. J. R. Quinlan, C4.5: Programs for machine learning. Morgan Kaufmann Publishers Inc., 1993.
64. Elvira Consortium, "Elvira: An environment for probabilistic graphical models," in Procs. of the 1st European Workshop on Probabilistics Graphical Models, 2002, pp. 222-230.
65. D. Heckerman, D. Geiger, and D. M. Chickering, "Learning Bayesian networks: The combination of knowledge and statistical data." Machine Learning, vol. 20, no. 3, pp. 197-243, 1995.
66. I. J. Good, The estimation of probabilities. MIT Press, 1965.
67. M. Friedman, "The use of ranks to avoid the assumption of normality implicit in the analysis of variance," Journal of the American Statistical Association, no. 32, pp. 675-701, 1937.
68. J. Demšar, "Statistical comparisons of classifiers over multiple data sets," Journal of Machine Learning Research, vol. 7, pp. 1-30, 2006. (Pubitemid 43022939)