Unsupervised Learning for Gene Regulation Network Inference from Expression Data: A Review

Algorithms in Computational Molecular Biology: Techniques, Approaches and Applications

Volumn , Issue , 2010, Pages 955-978

  Elati, Mohamed ^a   Rouveirol, Céline ^b  

^a Parc Scientifique de Luminy   (France)

^b UNIVERSITÉ PARIS 13   (France)

Author keywords

Formidable challenge, gene regulatory network dissection delineating how eukaryote cells coordinate and govern patterns of gene expression leading to a phenotype; Gene expression, data and analysis amount of mRNA produced during transcription, measure of how active or functional a gene is; Unsupervised learning for gene regulation network inference from expression data

Indexed keywords

EID: 84875740737     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/9780470892107.ch41     Document Type: Chapter

References (69)

1. R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. Proceedings of the International Conference on Management of Data, 1993, pp. 207-216.
2. E. Alm and A.P. Arkin. Biological networks. Curr Opin Struct Biol, 13:193-202, 2003.
3. S. Balaji, M.M. Babu, L.M. Iyer, N.M. Luscombe, and L. Aravind. Comprehensive analysis of combinatorial regulation using the transcriptional regulatory network of yeast. J Mol Biol, 360:204-212, 2006.
4. A.L. Barabasi and Z.N. Oltvai. Network biology: Understanding the cell's functional organization. Nat Rev Genet, 5:101-113, 2004.
5. K. Basso, A.A. Margolin, G. Stolovitzky, U. Klein, R. Dalla-Favera, and A. Califano. Reverse engineering of regulatory networks in human B cells. Nat Genet, 37:382-390, 2005.
6. C. Becquet, S. Blachon, B. Jeudy, J.F. Boulicaut, and O. Gandrillon. Strong-associationrule mining for large-scale gene-expression data analysis: A case study on human sage data. Genome Biol, 3: research0067.1research0067.16, 2002.
7. Y. Benjamini and Y. Hochberg. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J Roy Stat Soc, 57:289-300, 1995.
8. Y. Benjamini and Y. Yekutieli. The control of the false discovery rate in multiple testing under dependency. Ann Stat, 29:1165-1198, 2001.
9. J. Besson, C. Robardet, J.F. Boulicaut, and S. Rome. Constraint-based concept mining and its application to microarray data analysis. Intell Data Anal, 9:59-82, 2005.
10. E. Birmele, M. Elati, C. Rouveirol, and Ch. Ambroise. Identification of functional modules based on transcriptional regulation structure. BMC Proceedings, 2:S4, 2008.
11. S. Blachon, R.G. Pensa, J. Besson, C. Robardet, J.F. Boulicaut, and O. Gandrillon. Clustering formal concepts to discover biologically relevant knowledge from gene expression data. In Silico Biol, 7:467-483, 2007.
12. C. Borgelt. Efficient implementations of apriori and eclat. 1st Workshop of Frequent Item Set Mining Implementations, 2003.
13. A.P. Bradley. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recogn, 30:1145-1159, 1997.
14. P. Brazhnik, A. de la Fuente, and P. Mendes. Gene networks: How to put the function in genomics. Trends Biotechnol, 20:467-472, 2002.
15. A.J. Butte and I.S. Kohane. Mutual information relevance networks: Functional genomic clustering using pairwise entropy measurements. Pacific Symposium in Biocomputing, 2000, pp. 418-429.
16. T. Calders, C. Rigotti, and J.F. Boulicaut. A survey on condensed representations for frequent sets. Constraint-Based Mining and Inductive Databases, volume 3848, Springer, New York, 2004, pp. 64-80.
17. P. Carmona-Saez, M. Chagoyen, A. Rodriguez, O. Trelles, J. Carazo, and A. Pascual- Montano. Integrated analysis of gene expression by association rules discovery. BMC Bioinformatics, 7:54, 2006.
18. S.L. Carter, C.M. Brechbuhler, M. Griffin, and A.T. Bond. Gene co-expression network topology provides a framework for molecular characterization of cellular state. Bioinformatics, 20:2242-2250, 2004.
19. Y.H. Chang, Y.C.Wang, and B.S. Chen. Identification of transcription factor cooperativity via stochastic system model. Bioinformatics, 22:2276-2282, 2006.
20. J.M. Cherry, C. Adler, C. Ball, S.A. Chervitz, S.S. Dwight, E.T. Hester, Y. Jia, G. Juvik, T. Roe, M. Schroeder, S.Weng, and D. Botstein. SGD: Saccharomyces Genome Database. Nucleic Acids Res, 26:73-79, 1998.
21. D.M. Chickering. Optimal structure identification with greedy search. J Mach Learn Res, 3:507-554, 2002.
22. G. Cong, A.K.H. Tung, X. Xu, F. Pan, and J. Yang. FARMER: Finding interesting rule groups in microarray datasets. Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data, Paris, France, 2004, pp. 143-154.
23. C. Creighton and S. Hanash. Mining gene expression databases for association rules. Bioinformatics, 19:79-86, 2003.
24. B. Crémilleux and A. Soulet. Discovering knowledge from local patterns with global constraints. Proceedings of the International Conference on Computational Science and Its Applications, Part II, Perugia, Italy, Springer-Verlag, New York, 2008, pp. 1242-1257.
25. F. D'Alché Buc. Inference of biological regulatory networks: Machine learning approaches. In François Képès, editor, Biological Networks, World Scientific, Singapore, 2008, pp. 41-83.
26. A. de la Fuente, N. Bing, I. Hoeschele, and P. Mendes. Discovery of meaningful associations in genomic data using partial correlation coefficients. Bioinformatics, 20:3565-3574, 2004.
27. T. Van den Bulcke, K. Van Leemput, B. Naudts, P. van Remortel, H. Ma, A. Verschoren, B. De Moor, and K. Marchal. SynTReN: A generator of synthetic gene expression data for design and analysis of structure learning algorithms. BMC Bioinformatics, 7:43, 2006.
28. P. D'haeseleer, S. Liang, and R. Somogyi. Genetic network inference: From co-expression clustering to reverse engineering. Bioinformatics, 16:707-726, 2000.
29. J. Dougherty, R. Kohavi, and A. Sahami. Supervised and unsupervised discretization of continuous features. Machine Learning: Proceedings of the Twelfth International Conference, 1995, pp. 487-499.
30. S. Draghici. Data Analysis Tools for DNA Microarrays. Chapman&Hall/CRC, Boca Raton, FL, 2003.
31. M. Elati, P. Neuvial, M. Bolotin-Fukuhara, E. Barillot, F. Radvanyi, and C. Rouveirol. LICORN: Learning cooperative regulation networks from gene expression data. Bioinformatics, 23:2407-2414, 2007.
32. L. Elnitski, V.X. Jin, P.J. Farnham, and S.J.M. Jones. Locating mammalian transcription factor binding sites: A survey of computational and experimental techniques. Genome Res, 16(12):1455-1464, 2006.
33. J. Faith, B. Hayete, J.T. Thaden, I. Mogno, J. Wierzbowski, G. Cottarel, S. Kasif, J.J. Collins, and T.S. Gardner. Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol, 5:e8, 2007.
34. N. Friedman. Inferring cellular networks using probabilistic graphical models. Science, 303:799-805, 2004.
35. N. Friedman, M. Linial, I. Nachman, and D. Pe'er. Using Bayesian network to analyze expression data. Comput Biol, 7:601-620, 2000.
36. A.W. Fu, R.W. Kwong, and J. Tang. Mining n-most interesting itemsets. Proceedings of the 12th International Symposium Foundations Intelligent Systems (ISMIS), Springer- Verlag, New York, 2000, pp. 59-67.
37. E. Georgii, L. Richter, U. Ruckert, and S. Kramer. Analyzing microarray data using quantitative association rules. Bioinformatics, 21:123-129, 2005.
38. B. Goethals and M.J. Zaki. Advances in frequent itemset mining implementations: Introduction to FIMI03. FIMI, volume 90, 2003.
39. N. Guelzim, S. Bottani, P. Bourgine, and F. Képès. Topological and causal structure of the yeast transcriptional regulatory network. Nat Genet, 31:60-63, 2002.
40. L. Ji and K. L. Tan. Mining gene expression data for positive and negative co-regulated gene clusters. Bioinformatics, 20:2711-2718, 2004.
41. L. Ji and K. L. Tan. Identifying time-lagged gene clusters using gene expression data. Bioinformatics, 21:509-516, 2005.
42. T.I. Lee, N.J. Rinaldi, F. Robert, D.T. Odom, Z. Bar-Joseph, G.K. Gerber, N.M. Hannett, C.T. Harbison, C.M. Thompson, I. Simon, J. Zeitlinger, E.G. Jennings, H.L.Murray, D.B. Gordon, B. Ren, J.J. Wyrick, J.B. Tagne, T.L. Volkert, E. Fraenkel, D.K. Gifford, and R.A. Young. Transcriptional regulatory networks in Saccharomyces cerevisiae. Science, 298:799-804, 2002.
43. W.P. Lee and W.S. Tzou. Computational methods for discovering gene networks from expression data. Brief Bioinformatics, 10:408-423, 2009.
44. J. Liu, S. Paulsen,W.Wang, A. Nobel, and J. Prins.Mining approximate frequent itemsets from noisy data. Proceedings of the Fifth IEEE International Conference on Data Mining, IEEE Computer Society, Piscataway, NJ, 2005, 721-724.
45. N.M. Luscombe, M.M. Babu, H. Yu, M. Snyder, S.A. Teichmann, and M. Gerstein. Genomic analysis of regulatory network dynamics reveals large topological changes. Nature, 431:308-312, 2004.
46. H. Mannila and H. Toivonen. Levelwise search and borders of theories in knowledge discovery. Data Min Knowl Discov J, 1:241-258, 1997.
47. F. Markowetz and R. Spang. Inferring cellular networksa review. BMC Bioinformatics, 8 (Suppl 6):S5, 2007.
48. S. Chawla and T. McIntosh. High confidence rule mining for microarray analysis. IEEE/ACM Trans Comput Biol Bioinform, 4(4):611-623, 2007.
49. M. Middendorf, A. Kundaje, C. Wiggins, Y. Freund, and C. Leslie. Predicting genetic regulatory response using classification. Bioinformatics, 20:232-240, 2004.
50. T.M. Mitchell. Machine Learning. McGraw Hill, New York, 1997.
51. R.T. Ng, L.V.S. Lakshmanan, J. Han, and A. Pang. Exploratory mining and pruning optimizations of constrained associations rules. SIGMOD Rec, 27(2):13-24, 1998.
52. F. Pan, G. Cong, A.K.H. Tung, J. Yang, and M.J. Zaki. Carpenter: Finding closed patterns in long biological datasets. KDD '03: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2003, pp. 637-642.
53. N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal. Pruning closed itemset lattices for association rules. Actes 14ème Conférence Bases de Donnes Avances (BDA'98), 1998, pp. 177-196.
54. J. Pearl. On the connection between the complexity and credibility of inferred models. Int J Gen Syst, 4:255-264, 1978.
55. D. Pe'er, A. Regev, and A. Tanay. Minreg: Inferring an active regulator set. Bioinformatics, 18:258-267, 2002.
56. R.G. Pensa, C. Leschi, J. Besson, and J.F. Boulicaut. Assessment of discretization techniques for relevant pattern discovery from gene expression data. Proceedings of the 4th ACM SIGKDD Workshop on Data Mining in Bioinformatics BIOKDD'04, 2004, pp. 24-30.
57. T.H. Pham, K. Satou, and T.B. Ho. Mining yeast transcriptional regulatory modules from factor dna-binding sites and gene expression data. Genome Inform, 15:287-295, 2004.
58. M. Schena, D. Shalon, R. Davis, and P. Brown. Quantitative monitoring of gene expression patterns with a complementary dna microarray. Science, 70:467-470, 1995.
59. E. Segal, M. Shapira, A. Regev, D. Pe'er, D. Botstein, D. Koller, and N. Friedman. Module networks: Identifying regulatory modules and their condition-specific regulators from gene expression data. Nat Genet, 34:166-176, 2003.
60. V.A. Smith, E.D. Jarvis, and A.J. Hartemink. Evaluating functional network inference using simulations of complex biological systems. Bioinformatics, 18:216S-224S, 2002.
61. R. Somogyi and H. Kitano. Gene expression and genetic networks. Pacific Symposium in Biocomputing, 1999, pp. 3-4.
62. P. Spirtes, C. Glymour, R. Scheines, S. Kauffman, V. Aimale, and F. Wimberly. Constructing Bayesian network models of gene expression networks from microarray data. Proceedings of the Atlantic Symposium on Computational Biology, Genome Information Systems and Technology, 2000.
63. C. Stark, B.J. Breitkreutz, T. Reguly, L. Boucher, A. Breitkreutz, andM. Tyers. BioGRID: A general repository for interaction datasets. Nucleic Acids Res, 34:535-539, 2006.
64. R. Steuer, J. Kurths, C.O. Daub, J. Weise, and J. Selbig. The mutual information: Detecting and evaluating dependencies between variables. Bioinformatics, S18:231-240, 2002.
65. M.C. Teixeira, P. Monteiro, P. Jain, S. Tenreiro, A.R. Fernandes, N.P. Mira, M. Alenquer, A.T. Freitas, A.L. Oliveira, and I. Sa-Correia. The YEASTRACT database: A tool for the analysis of transcription regulatory associations in Saccharomyces cerevisiae. Nucleic Acids Res, 34:446-451, 2006.
66. L. Torgo. Functional models for regression tree leaves. Proceedings of the 14th International Conference on Machine Learning, Morgan Kaufmann, San Francisco, CA, 1997, pp. 385-393.
67. R.S. Wang, X.S. Zhang, and L. Chen. Inferring transcriptional interactions and regulator activities from experimental data. Mol Cells, 24:307-15, 2007.
68. F.C. Wimberly, T. Heiman, J. Ramsey, and C. Glymour. Experiments on the accuracy of algorithms for inferring the structure of genetic regulatory networks from microarray expression levels. Proceedings of the IJCAI 2003 Bioinformatics Workshop, 2003.
69. X. Zhang and W. Wang. An efficient algorithm for mining coherent patterns from heterogeneous microarrays. 19th International Conference on Scientific and Statistical Database Management, 2007, SSBDM '07, 2007, p. 32.