Mining high-correlation association rules for inferring gene regulation networks

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5691 LNCS, Issue , 2009, Pages 244-255

  Shang, Xuequn ^a   Zhao, Qian ^a   Li, Zhanhuai ^a  

^a NORTHWESTERN POLYTECHNICAL UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ASSOCIATION RULE MINING; BIOLOGICAL PROCESS; DATA SETS; GENE EXPRESSION MICROARRAY; GENE REGULATION NETWORK; MICROARRAY DATA; MINING ASSOCIATION RULES; MOLECULAR MECHANISM; NOVEL ALGORITHM; SAMPLE SIZES;

ALGORITHMS; ASSOCIATION RULES; ASSOCIATIVE PROCESSING; DATA MINING; GENE EXPRESSION; MINING;

DATA WAREHOUSES;

EID: 70349307041     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-03730-6_20     Document Type: Conference Paper

References (24)

1. Aggarwal, C., Procopiuc, C., Yu, P.: Finding localized associations in market basket data. IEEE Trans. Knowl. Data Eng. 14(1), 51-62 (2002)
2. Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Proc. ACM SIGMOD International Conference on Management of Data, pp. 207-216 (1993)
3. Albert, R.: Scale-free networks in cell biology. Journal Cell Sci. 118, 4947-4957 (2005)
4. Barabasi, A., Oltvai, Z.: Network biology: understanding the cells functional organization. Nat. Rev. Genet. 5, 101-113 (2004)
5. Becquet, C., Blachon, S., Jeudy, J.B.B., Gandrillon, O.: Strong-association-rule mining for large-scale gene-expression data analysis: a case study on human sage data. Genome Biology 3(12) (2002)
6. Cheng, Y., Church, G.M.: Biclustering of expression data. In: Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology, pp. 93-103 (2000)
7. Cong, G., Tan, K.L., Tung, A., Pan, F.: Mining frequent closed patterns in microarray data. In: Proc. Fourth IEEE Intl. Conf. Data Mining (ICDM), pp. 363-366 (2004)
8. The Gene Ontology Consortium. The gene ontology (go) database and informatics resource. Nucleic Acids Research 32, 258-261 (2004)
9. Creighton, C., Hanash, S.: Mining gene expression databases for association rules. Bioinformatics 19, 79-869 (2003)
10. Cho, R.J., et al.: A genome-wide transcriptional analysis of the mitotic cell cycle. Molecular Cell 2(1), 65-73 (1998)
11. Friedman, N., Linial, M., Nachman, I., Pe'er, D.: Using bayesian network to analyze expression data. Journal of Computational Biology 7, 601-620 (2000)
12. Gasch, A., Eisen, M.: Exploring the conditional coregulation of yeast gene expression through fuzzy k-means clustering. Genome Biol. 3 (2002)
13. Huang, Z., Li, J., Su, H., Watts, G., Chen, H.: Large-scale regulatory network analysis from microarray data: modified bayesian network learning and association rule mining. Decis. Support Syst. 43(4), 1207-1225 (2007)
14. Kotlyar, M., Jurisica, I.: Predicting protein-protein interactions by association mining. Information Systems Frontiers 8(1), 37-47 (2006)
15. Murphy, K., Mian, S.: Modeling gene expression data using dynamic bayesian networks. In: Technical Report, Computer Science Division, University of California, Berkeley (1999)
16. Oba, S., Sato, M., Takemasa, I., et al.: A bayesian missing value estimation method for gene expression profile data. Bioinformatics 19(16), 2088-2096 (2003)
17. Oyama, T., Kitano, K., Satou, K., Ito, T.: Extraction of knowledge on protein protein interaction by association rule discovery. Bioinformatics 18(5), 705-714 (2002)
18. Pan, F., Cong, G., Tung, K., Yang, J., Zaki, M.: Carpenter: Finding closed patterns in long biological datasets. In: Proc. ACM SIGKDD Intl. Conf. Knowledge Discovery and Data Mining (KDD), pp. 637-642 (2003)
19. Pandey, G., Steinbach, M., Gupta, R., Garg, T., Kumar, V.: Association analysisbased transformations for protein interaction networks: A function prediction case study. In: Proc. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 540-549 (2007)
20. Tsay, Y., Chang-Chien, Y.: An efficient cluster and decomposition algorithm for mining association rules. Inf. Sci. 160, 161-170 (2004)
21. Tuzhilin, A., Adomavicius, G.: Handling very large numbers of association rules in the analysis of microarray data. In: Proc. of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 23-26 (2002)
22. Wahde,M., Hertz, J.: Modeling genetic regulatory dynamics in neural development. Journal of Computational Biology 8, 14863-14868 (2001)
23. Xiong, H., He, X., Ding, C., Zhang, Y., Kumar, V., Holbrook, S.R.: Identification of functional modules in protein complexes via hyperclique pattern discovery. In: Proc. Pacific Symposium on Biocomputing (PSB), pp. 221-232 (2005)
24. Yeung, K., Medvedovic, M., Bumgarner, R.: From co-expression to co-regulation: how many microarray experiments do we need? Genome Biol. 5(7) (2004)