What is unequal among the equals? Ranking equivalent rules from gene expression data

IEEE Transactions on Knowledge and Data Engineering

Volumn 23, Issue 11, 2011, Pages 1735-1747

  Cai, Ruichu ^a,b   Tung, Anthony K H ^c   Zhang, Zhenjie ^c   Hao, Zhifeng ^a  

^a GUANGDONG UNIVERSITY OF TECHNOLOGY   (China)

^b SOUTH CHINA UNIVERSITY OF TECHNOLOGY   (China)

^c NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

Association rules; gene expression data; incremental mining framework; robust classification

Indexed keywords

CLASSIFICATION MODELS; DATA SETS; EMPIRICAL STUDIES; GENE EXPRESSION DATA; HIGH-DIMENSIONAL; INCREMENTAL MINING; INTERESTING RULES; INTERESTINGNESS; INTERESTINGNESS MEASURES; LOWER BOUNDS; ROBUST CLASSIFICATION; RULE EXTRACTION; SUPPORT AND CONFIDENCE;

ASSOCIATION RULES;

GENE EXPRESSION;

EID: 80053272338     PISSN: 10414347     EISSN: None     Source Type: Journal    
DOI: 10.1109/TKDE.2010.207     Document Type: Article

References (41)

1. www.csie.ntu.edu.tw/cjlin/libsvm, 2011.
2. R. Agrawal et al., "Mining Association Rules between Sets of Items in Large Databases," Proc. ACM SIGMOD Int'l Conf. Management of Data (SIGMOD '93), 1993.
3. S. Brin et al., "Dynamic Itemset Counting and Implication Rules for Market Basket Data," Proc. ACM SIGMOD Int'l Conf. Management of Data (SIGMOD '97), 1997.
4. M.P.S. Brown et al., "Knowledge-Based Analysis of Microarray Gene Expression Data by Using Support Vector Machines," Proc. Nat'l Academy of Sciences USA, vol. 97, no. 1, pp. 262-267, 2000. (Pubitemid 30055819)
5. H. Cheng et al., "Discriminative Frequent Pattern Analysis for Effective Classification," Proc. IEEE 23rd Int'l Conf. Data Eng. (ICDE), 2007.
6. H. Cheng et al., "Direct Discriminative Pattern Mining for Effective Classification," Proc. IEEE 24th Int'l Conf. Data Eng. (ICDE), 2008.
7. G. Cong et al., "Mining Top-k Covering Rule Groups for Gene Expression Data," Proc. ACM SIGMOD Int'l Conf. Management of Data (SIGMOD '05), 2005.
8. G. Cong et al., "Farmer: Finding Interesting Rule Groups in Microarray Datasets," Proc. ACM SIGMOD Int'l Conf. Management of Data (SIGMOD '04), 2004.
9. C. Creighton and S. Hanash, "Mining Gene Expression Databases for Association Rules," Bioinformatics, vol. 19, no. 1, pp. 79-86, 2003. (Pubitemid 36150183)
10. A. Dent, "B-cell Lymphoma: Suppressing a Tumor Suppressor," Nature Medicine, vol. 11, no. 1, p. 22, 2005. (Pubitemid 40215829)
11. T. Golub et al., "Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring," Science, vol. 286, no. 5439, pp. 531-537, 1999.
12. G.J. Gordon et al., "Translation of Microarray Data into Clinically Relevant Cancer Diagnostic Tests Using Gene Expression Ratios in Lung Cancer and Mesothelioma," Cancer Research, vol. 62, no. 17, pp. 4963-4967, 2002. (Pubitemid 34984420)
13. M. Iwen et al., "Scalable Rule-Based Gene Expression Data Classification," Proc. IEEE 24th Int'l Conf. Data Eng. (ICDE '08), 2008.
14. R. Bayardo, R. Agrawal, and D. Gunopulos, "Constraint-Based Rule Mining in Large, Dense Databases," Proc. 15th Int'l Conf. Data Eng. (ICDE), pp. 188-197, 1999.
15. D. Koczan et al., "Gene Expression Profiling of Peripheral Blood Mononuclear Leukocytes from Psoriasis Patients Identifies New Immune Regulatory Molecules," European J. Dermatology, vol. 15, no. 4, pp. 251-258, 2005.
16. R. Kohavi, "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection," Proc. 14th Int'l Joint Conf. Artificial Intelligence (IJCAI '95), 1995.
17. J. Li et al., "Minimum Description Length Principle: Generators are Preferable to Closed Patterns," Proc. 21st Nat'l Conf. Artificial Intelligence (AAAI '06), 2006.
18. W. Li et al., "CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules," Proc. IEEE Int'l Conf. Data Mining (ICDM), 2001.
19. B. Liu et al., "Integrating Classification and Association Rule Mining," Proc. Knowledge Discovery and Data Mining (KDD), 1998.
20. X. Liu et al., "An Entropy-Based Gene Selection Method for Cancer Classification Using Microarray Data," BMC Bioinformatics, vol. 6, no. 1, p. 76, 2005.
21. G. Mulligan et al., "Gene Expression Profiling and Correlation with Outcome in Clinical Trials of the Proteasome Inhibitor Bortezomib," Blood, vol. 109, no. 8, pp. 3177-3188, 2007. (Pubitemid 46572502)
22. F. Pan et al., "Carpenter: Finding Closed Patterns in Long Biological Datasets," Proc. Ninth ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD '03), 2003.
23. M. Seimiya et al., "Clast5/stra13 is a Negative Regulator of B Lymphocyte Activation," Biochemical and Biophysical Research Comm., vol. 292, no. 1, pp. 121-127, 2002. (Pubitemid 34687338)
24. M. Seimiya et al., "Impaired Lymphocyte Development and Function in Clast5/Stra13/Dec1-Transgenic Mice," European J. Immunology, vol. 34, no. 5, pp. 1322-1332, 2004. (Pubitemid 39256011)
25. M. Shipp et al., "Diffuse large B-cell Lymphoma Outcome Prediction by Gene-Expression Profiling and Supervised Machine Learning," Nature Medicine, vol. 8, no. 1, pp. 68-74, 2002. (Pubitemid 34101731)
26. D. Singh et al., "Gene Expression Correlates of Clinical Prostate Cancer Behavior," Cancer Cell, vol. 1, no. 2, pp. 203-209, 2002. (Pubitemid 41039166)
27. M. Takagi et al., "Regulation of p53 Translation and Induction After DNA Damage by Ribosomal Protein l26 and Nucleolin," Cell, vol. 123, no. 1, pp. 49-63, 2005. (Pubitemid 41414528)
28. G.I. Webb, "Discovering Significant Rules," Proc. 12th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD '06), 2006.
29. G.I. Webb, "Discovering Significant Patterns," Machine Learning, vol. 71, no. 1, p. 131, 2008.
30. X. Yin and J. Han, "CPAR: Classification Based on Predictive Association Rules," Proc. SIAM Int'l Conf. Data Mining (SDM), 2003.
31. M.J. Zaki and K. Gouda, "Fast Vertical Mining Using Diffsets," Proc. Ninth ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD '03), 2003.
32. A. Reiner, D. Yekutieli, and Y. Benjamini, "Identifying Differentially Expressed Genes Using False Discovery Rate Controlling Procedures," Bioinformatics, vol. 19, no. 3, pp. 368-375, 2003. (Pubitemid 36284936)
33. G. Pandey, G. Atluri, M. Steinbach, and V. Kumar, "Association Analysis Techniques for Discovering Functional Modules from Microarray Data," Nature Precedings, 2008.
34. S. Dudoit, Y.H. Yang, M.J. Callow, and T.P. Speed, "Statistical Methods for Identifying Differentially Expressed Genes in Replicated cDNA Microarray Experiments," Statistica Sinica, vol. 12, pp. 111-139, 2002.
35. B. Efron, R. Tibshirani, J.D. Storey, and V. Tusher, "Empirical Bayes Analysis of a Microarray Experiment," J. Am. Statistical Assoc., vol. 96, pp. 1151-1160, 2001. (Pubitemid 33695570)
36. D. Jiang, C. Tang, and A. Zhang, "Cluster Analysis for Gene Expression Data: A Survey," IEEE Trans. Knowledge and Data Eng., vol. 16, no. 11, pp. 1370-1386, Nov. 2004. (Pubitemid 40242326)
37. M. Schena et al., "Quantitative Monitoring of Gene Expression Patterns with a Complementary DNA Microarray," Science, vol. 270, no. 5235, pp. 467-470, 1995.
38. N. Friedman, I. Nachman, and D. Pe'er, "Using Bayesian Networks to Analyze Expression Data," J. Computational Biology: Computational Moleculer Cell Biology, vol. 7, pp. 601-620, 2000.
39. H.C. Wang and Y.S. Lee, "Gene Network Prediction from Microarray Data by Association Rule and Dynamic Bayesian Network," Proc. Int'l Conf. Computational Science and Its Applications (ICCSA), pp. 309-317, 2005.
40. X.Q. Shang, Q. Zhao, and Z.H. Li, "Mining High-Correlation Association Rules for Inferring Gene Regulation Networks," Proc. 11th Int'l Conf. Data Warehousing and Knowledge Discovery (DaWaK '09), pp. 244-255, 2009.
41. http://www.comp.nus.edu.sg/̃atung/lbGene.zip, 2011.