Gene association analysis: A survey of frequent pattern mining from gene expression data

Briefings in Bioinformatics

Volumn 11, Issue 2, 2009, Pages 210-224

  Alves, Ronnie ^a   Rodriguez Baena, Domingo S ^b   Aguilar Ruiz, Jesus S ^b,c  

^a UNIVERSITÉ CÔTE D'AZUR   (France)

^b UNIVERSIDAD PABLO DE OLAVIDE   (Spain)

^c BioData Mining Journal ^*  (United Kingdom)

Author keywords

Frequent pattern mining; Gene association analysis; Gene expression analysis

Indexed keywords

ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; BIOLOGY; DNA MICROARRAY; DNA SEQUENCE; GENE EXPRESSION; GENE EXPRESSION PROFILING; GENE REGULATORY NETWORK; METHODOLOGY;

ALGORITHMS; COMPUTATIONAL BIOLOGY; GENE EXPRESSION; GENE EXPRESSION PROFILING; GENE REGULATORY NETWORKS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PATTERN RECOGNITION, AUTOMATED; SEQUENCE ANALYSIS, DNA;

EID: 77950925363     PISSN: 14675463     EISSN: 14774054     Source Type: Journal    
DOI: 10.1093/bib/bbp042     Document Type: Article

References (47)

1. Jiang D, Tang C, Zhang A. Cluster analysis for gene expression data: a Survey. IEEE Trans Knowl Data Eng 2004;16: 1370-86.
2. Madeira SC, Oliveira AL. Biclustering algorithms for biological data analysis: a survey. IEEE/ACMTrans Comput Biol Bioinform 2004;1:24-45.
3. Becquet C, Blachon S, Jeudy B, et al. Strong-associationrule mining for large-scale gene-expression data analysis: a case study on human SAGE data. Genome Biol 2002;3:12.
4. Carmona-Saez P, Chagoyen M, Rodriguez A, et al. Integrated analysis of gene expression by association rules discovery. BMC Bioinformatics 2006;7:54.
5. Creighton C, Hanash S. Mining gene expression databases for association rules. Bioinformatics 2003;19:79-86.
6. Bebek G, Yang J. PathFinder: mining signal transduction pathway segments from protein-protein interaction networks. BMC Bioinformatics 2007;8:335.
7. Lopez FJ, Blanco A, Garcia F, et al. Fuzzy association rules for biological data analysis: a case study on yeast. BMC Bioinformatics 2008;9:107.
8. Ceglar A, Roddick JF. Association mining. ACM Comput Surv 2006;38:2.
9. Agrawal R, Imielinski T, Swami A. Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACMSIGMOD International Conference on Management of Data,. Washington, DC, USA: ACM Press, 1993;207-16.
10. Berrar DP, Dubitzky W, Granzow M, et al. A Practical Approach to Microarray Data Analysis, Vol. 3. Heidelberg, Germany: Springer 2006;12.
11. Yang G. The complexity of mining maximal frequent itemsets and maximal frequent patterns. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. Seattle, Washington, USA: ACM Press, 2004;344-53.
12. Pensa RG, Leschi C, Besson J, et al. Assessment of discretization techniques for relevant pattern discovery from gene expression data. In: Proceedings of 4th Workshop on DataMining in Bioinformatics BIOKDD. Seattle, Washington, USA, 2004;24-30.
13. Kotala P, Perera A, Zhou JK, et al. Gene expression profiling of DNA microarray data using peano count tree (p-trees). In: Proceedings of the First Virtual Conference on Genomics and Bioinformatics. North Dakota State University, USA, 2001; 15-16.
14. Ponzoni I, Azuaje F, Augusto JC, et al. Inferring adaptive regulation thresholds and association rules from gene expression data through combinatorial optimization learning. IEEE/ACM Trans Comput Biol Bioinform 2007;4: 624-33.
15. Tuzhilin A, Adomavicius G. Handling very large numbers of association rules in the analysis of microarray data. In: Proceedings of the The Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Edmonton, Alberta, Canada: ACM Press, 2002;396-404.
16. Hu M, Choi K, Su W, et al. A gene pattern mining algorithm using interchangeable gene sets for prokaryotes. BMC Bioinformatics 2008;9:124.
17. Dougherty J, Kohavi R, Sahami M. Supervised and unsupervised discretization of continuous features. In: International Conference on Machine Learning. Tahoe City, California, USA: Morgan Kaufmann, 1995;194-202.
18. Fayya and Irani. Multi-interval discretization of continuousvalued attributes for classification learning. In: Proceedings of the International Joint Conference on Uncertainty in AI. Chambéry, France: Morgan Kaufmann, 1993;1022-27.
19. Zhang Z, Teo A, Chin-Ooi B, et al. Mining deterministic biclusters in gene expression data. In: Proceedings of the 4th IEEE Symposium on Bioinformatics and Bioengineering. Taichung, Taiwan: IEEE Computer Society, 2004;283-90.
20. Ismail M.K., Ciesielski V. An empirical investigation of the impact of discretization on common data distributions. In: Proceedings of the Third International Conference on Hybrid Intelligent Systems. Melbourne, Australia: IOS Press, 2003; 692-701.
21. Martinez R, Pasquier C, Pasquier N. GenMiner: mining informative association rules from genomic data. In: Proceedings of the IEEE BIBM International Conference on Bioinformatics and Biomedecine. Silicon Valley, CA, USA: IEEE Computer Society, 2007;15-22.
22. Han J, Pei J, Yin Y. Mining frequent patterns without candidate generation. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data. Dallas, Texas, USA: ACM Press, 2000;1-12.
23. Han J, Cheng H, Xin D, et al. Frequent pattern mining: current status and future directions. Data Min Knowl Discov 2007;15:55-86.
24. Bayardo RJ. Efficiently mining long patterns from databases. In: Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data. Seattle, Washington, USA: ACM Press, 1998;88-93.
25. Gouda K, Zaki MJ. GenMax: an efficient algorithm for mining maximal frequent itemsets. Data Min Knowl Discov 2005;11:223-42.
26. Pasquier N, Bastide Y, Taouil R, et al. Efficient mining of association rules using closed itemset lattices. Inf Syst 1999; 24:25-46.
27. Wang J, Han J, Pei J. CLOSET+: searching for the best strategies for mining frequent closed itemsets. In: Proceedings of theThe Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Washington, DC, USA: ACM Press, 2003;236-45.
28. Zaki MJ, Hsiao CJ. CHARM: an efficient algorithm for closed itemset mining. In: Proceedings of the SIAM International Conference on Data Mining. Arlington, VA, USA: SIAM Press, 2002;457-73.
29. Pan F, Cong G, Tung AK, et al. Carpenter: finding closed patterns in long biological datasets. In: Proceedings of the the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Washington, DC, USA: ACM Press, 2003;637-42.
30. Pan F, Tung A, Cong G, et al. COBBLER: combining column and row enumeration for closed pattern discovery. In: Proceedings of the 16th International Conference on Scientific and Statistical Database Management SSDBM. Santorini Island, Greece: IEEE Computer Society, 2004; 21-30.
31. Cong G, Tung AK, Xu X, et al. FARMER: finding interesting rule groups in microarray datasets. In: Proceedings of the ACM SIGMOD International Conference on Management of Data. Paris, France: ACM Press, 2004; 143-54.
32. Cong G, Tan K, Tung AK, et al. Mining top-K covering rule groups for gene expression data. In: Proceedings of the ACM SIGMOD International Conference on Management of Data. Baltimore, Maryland, USA: ACM Press, 2005; 670-81.
33. Liu H, Han J, Xin D, et al. Top-down mining of interesting patterns from very high dimensional data. In: Proceedings of the 22nd International Conference on Data Engineering. Atlanta, GA, USA: IEEE Computer Society, 2006.
34. Zhu F, Yan X, Han J, et al. Mining colossal frequent patterns by core pattern fusion. In: Proceedings of the IEEE 23rd International Conference on Data Engineering. Istanbul, Turkey: ACM Press, 2007;706-15.
35. McIntosh T, Chawla S. High confidence rule mining for microarray analysis. IEEE/ACM Trans Comput Biol Bioinform 2007;4:611-23.
36. Hughes T, Marton M, Jones A, et al. Functional discovery via a compendium of expression profiles. Cell 2000;102: 109-26.
37. Ashburner M, Ball CA, Blake JA, et al. Gene ontology: tool for the unification of biology. Nat Genet 2000;25:25-9. Gene association analysis 223
38. Kanehisa M, Goto S, Hattori M, et al. From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res 2006;32:354-57.
39. Alfarano C, Andrade CE, Anthony K, Bahroos N, et al. The biomolecular interaction network database and related tools. Nucleic Acids Res 2005;33:D418-24.
40. Georgii E, Richter L, Rückert U. Analyzing microarray data using quantitative association rules. Bioinformatics 2005;11:123-9.
41. Tan P, Kumar V, Srivastava J. Selecting the right interestingness measure for association patterns. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Edmonton, Alberta, Canada: ACM Press, 2002;22-41.
42. Martins L, Jensen L, Simon J, et al. Metalloregulation of FRE1 and FRE2 homologs in Saccharomyces cerevisiae. J Biol Chem 1998;273:716-21.
43. Nam H, Lee K, Lee D. Identification of temporal association rules from time-series microarray data sets. BMC Bioinformatics 2009;10:3.
44. Wang SM. Understanding SAGE data. Trends Genet 2007; 23:42-50.
45. Carmona-Saez P, Chagoyen M, Tirado F, et al. GENECODIS: a web-based tool for finding significant concurrent annotations in gene lists. Genome Biol 2007;8:R3.
46. Mnaimneh S, Davierwala AP, Haynes J, et al. Exploration of essential gene functions via titratable promoter alleles. Cell 2004;118:31-44.
47. Spellman P, Sherlock G, Zhang M, et al. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Biol Cell 1998;9:3273-97.