GenMiner: Mining informative association rules from genomic data

Proceedings - 2007 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2007

Volumn , Issue , 2007, Pages 15-22

  Martinez, Ricardo ^a   Pasquier, Claude ^b   Pasquier, Nicolas ^a  

^a UNIVERSITY OF NICE SOPHIA ANTIPOLIS   (France)

^b CENTRE DE BIOCHIMIE   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ASSOCIATIVE PROCESSING; BIOCOMMUNICATIONS; BIOINFORMATICS; DATA REDUCTION; GENE EXPRESSION; INFORMATION SCIENCE; LAWS AND LEGISLATION;

BIOLOGICAL INFORMATION; BIOLOGICAL RELATIONSHIPS; CLOSED ITEMSETS; DISCRETIZATION METHODS; GENOMIC DATA; INFORMATIVE ASSOCIATION RULES; INTERNATIONAL CONFERENCES; MICROARRAY DATASETS; QUALITATIVE INFORMATION;

ASSOCIATION RULES;

EID: 49049091935     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/BIBM.2007.49     Document Type: Conference Paper

References (33)

1. R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In Proc. VLDB conf., pages 478-499, 1994.
2. R. Altman and S. Raychaudhuri. Whole-genome expression analysis: challenges beyond clustering. Current Opinion Structural Biology, 11:340-347, 2001.
3. A. Bera and C. Jarque. Efficient tests for normality, homoscedasticity and serial independence of regression residuals: Monte carlo evidence. Economics Letters, 7:313-318, 1981.
4. S. Brin, R. Motwani, J. D. Ullman, and S. Tsur. Dynamic itemset counting and implication rules for market basket data. In Proc. ACM SIGMOD conf., pages 255-264, 1997.
5. P. Carmona-Saez, M. Chagoyen, A. Rodriguez, O. Trelles, J. Carazo, and A. Pascual-Montano. Integrated analyis of gene expression by association rules discovery. BMC Bioinformatics, 7(54), 2006.
6. C. Creighton and S. Hanansh. Mining gene expression databases for association rules. Bioinformatics, 19:79-86, 2003.
7. L. Cristofor and D. A. Simovici. Generating an informative cover for association rules. In Proc. ICDM conf., pages 597-600, 2002.
8. A. davierwala, J. Haynes, B. Li, R. Brost, M. Robinson, and L. e. a. Yu. The synthetic genetic interation spectrum of essential genes. Nature Genetics, 37(10):1147-52, 2005.
9. J. DeRisi, L. Iyer, and V. Brown. Exploring the metabolic and genetic control of gene expression on a genomic scale. Science, 278:680-686, 1997.
10. M. Eisen, P. Spellman, P. Brown, and D. Botsein. Cluster analysis and display of genome wide expression patterns. In Proc. National Academy of Sciences USA, volume 95, pages 14863-8, 1998.
11. A. Gasch and M. Eisen. Exploring the conditional corregulation of yeast gene expression through fuzzy k-means clustering. Genome Biology, 3:1-22, 2002.
12. E. Georgi, L. Richter, U. Ruckert, and S. Kramer. Analyzing microarray data using quantitative association rules. Bioinformatics, 21:123-129, 2005.
13. F. Grubbs. Procedures for detecting outlying observations in samples. Technometrics, 11:1-21, 1969.
14. D. Hanisch, A. Zien, R. Zimmer, and T. Lengauer. Co-clustering of biological networks and gene expression data. Bioinformatics, 18:S145-S154, 2002.
15. L. Ji and K. Tan. Mining gene expression data for positive and negative co-regulated gene clusters. Bioinformatics, 20:2711-2718, 2004.
16. S. Kim and D. Volsky. Page: Parametric analysis of gene set enrichment. BMC Bioinformatics, 6:144, 2005.
17. T. Lee, N. Rinaldi, F. Robert, D. Odom, Z. Bar-Joseph, G. Gerber, N. Hannett, C. Harbison, C. Thompson, I. Simon, J. Zeitlinger, G. Jennings, H. Murray, B. Gordon, and R. Young. Transcriptional regulatory networks in saccharomyces cerevisiae. Science, 298(5594):799-804, 2002.
18. H. Lilliefors. On the kolmogorov-smirnov test for normality with mean and variance unknown. Journal of the American Statistical Association, 62, 1967.
19. R. Little and D. Rubin. Statistical Analysis with Missing Data. Wiley and Sons, 2 edition, 2002.
20. J. Liu, J. Yang, and W. Wang. Gene ontology friendly biclustering of expression profiles. In Proc. CSB conf., pages 436-447, 2004.
21. R. Martinez and M. Collard. Extracted knowledge: Interpretation in mining biological data, a survey. International Journal of Comp. Science and Applications: Special issue in Research Challenges in Information Science, 1:1-21, 2007.
22. R. Martinez, N. Pasquier, C. Pasquier, M. Collard, and L. Lopez-Perez. Co-expressed gene groups analysis (cgga): An automatic tool for the interpretation of microarray experiments. J. of Integrative Bioinformatics, 3(11):1-12, 2006.
23. R. H. Morse. Rap, rap, open up! new wrinkles for rapi in yeast. Trends Genet, 16:51-53, 2000.
24. NIST. e-Handbook of Statistical Methods. SEMATECH, 2007. http://www.itl.nist.gov/div898/handbook/.
25. K. Pan, C. Lih, and N. Cohen. Effects of threshold choice on biological conclusions reched during analysis of gene expression by dna microarrays. National Academy of Sciences PNAS, 102:8961-8965, 2005.
26. C. Pasquier, F. Girardot, K. Jevardat, and R. Christen. Thea: Ontology-driven analysis of microarray data. Bioinformatics, 20(16), 2004.
27. N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal. Efficient mining of association rules using closed itemset lattices. Information Systems, 24(1):25-46, 1999.
28. N. Pasquier, R. Taouil, Y. Bastide, G. Stumme, and L. Lakhal. Generating a condensed representation for association rules. Journal of Intelligent Information Systems, 24(1):29-60, January 2005.
29. P. Pehkonen, G. Wong, and P. Toronen. Theme discovery from gene lists for identification and viewing of multiple functional groups. BMC Bioinformatics, 6:162, 2005.
30. H. Shatkay, S. Edwards, W. W., and B. M. Genes, themes, microarrays: using information retrieval for large-scale gene analysis. In Proc. ISMB conf., pages 340-347, 2000.
31. A. Tuzhilin and G. Adomavicius. Handling very large numbers of association rules in the analysis of microarray data. In Proc. ACM SIGKDD conf., pages 396-404, 2002.
32. I. Yang, E. Chen, J. Hasseman, W. Liang, B. Frank, V. Sharov, and J. Quackenbush. Within the fold: assesing differential expression measures and reproducibility in microarray assays. Genome Biology, 3:11, 2002.
33. Y. Zhao, K. McIntosh, D. Rudra, S. Schawalder, D. Shore, and J. Warner. Fine-structure analysis of ribosomal protein gene transcription. Mollecular Cellular Biology, 26(13):4853-62, 2006.