An efficient algorithm for mining coherent patterns from heterogeneous microarrays

Proceedings of the International Conference on Scientific and Statistical Database Management, SSDBM

Volumn , Issue , 2007, Pages

  Zhang, Xiang ^a   Wang, Wei ^a  

^a Brooks Computer Science Building   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADMINISTRATIVE DATA PROCESSING; BIOACTIVITY; BIOCHIPS; CHEMOTHERAPY; CLUSTERING ALGORITHMS; DATABASE SYSTEMS; DRUG DELIVERY; DRUG DOSAGE; DRUG INTERACTIONS; GENE EXPRESSION; GENES; KETONES; MANAGEMENT INFORMATION SYSTEMS; MICROARRAYS; MINING; NUCLEIC ACIDS; ORGANIC ACIDS; STANDARDS; STATISTICAL METHODS; STATISTICS;

DATA COLLECTED; DATABASE MANAGEMENT; DISEASE DIAGNOSIS; DNA MICRO ARRAYS; DRUG DESIGNS; EFFICIENT ALGORITHMS; EXPERIMENTAL RESULTS; EXPRESSION LEVELS; GENE DISCOVERY; GENE EXPRESSION (MICROARRAY) DATA; HYBRID APPROACHES; INTERNATIONAL CONFERENCES; MICRO-ARRAY CHIPS; MONITOR (CO); SUBSPACE CLUSTERING;

BIOASSAY;

EID: 46649103266     PISSN: 10993371     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SSDBM.2007.30     Document Type: Conference Paper

References (29)

1. C. Agarwal, C. Procopiuc, J. Wolf, P. Yu, and J. Park. Fast algorithms for projected clustering. In SIGMOD, 1999.
2. C. Aggarwal and P. Yu. Finding generalized projected clusters in high dimensional spaces. In SIGMOD, 2000.
3. R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan. Automatic subspace clustering of high dimensional data for data mining applications. In SIGMOD, 1998.
4. A. Alizadeh and et al. Distinct types of diffuse large b-cell lymphoma identified by gene expression profiling. Nature, 403:503-11, 2000.
5. U. Alon, N. Barkai, D. Notterman, K. Gish, S. Ybarra, D. Mack, and A. Levine. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide array. In Proc. Natl. Acad. Sci. USA, volume 96, pages 6745-50, 1999.
6. M. Ashburner and et al. Gene ontology: tool for the unification of biology. The gene ontology consortium, Nat. Genet., 25:25-29, 2000.
7. A. Ben-Dor, R. Shamir, and Z. Yakhini. Clustering gene expression patterns. Journal of Comp. Bio., 6:281-97, 1999.
8. Y. Cheng and G. Church. Biclustering of experssion data. In Proc. of the ISMB, 2000.
9. P. D'haeseleer, X. Wen, S. Fuhrman, and R. Somogyi. Mining the gene expression matrix: inferring gene relationships from large scale gene expression data. Info. Process. in Cells and Tissues, pages 203-12, 1998.
10. M. Eisen, P. Spellman, P. Brown, and D. Botstein. Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA, 95:14863-68, 1998.
11. N. Friedman and et al. Using bayesian network to analyze expression data. In ISMB, 2000.
12. O. Griffith and et al. Assessment and integration of publicly available sage, cdna microarray, and oligonucleotide microarray expression data for global coexpression analyses. Genomics, 86(4):476-48, 2005.
13. H. Hu, X. Yan, Y. Huang, J. Han, and X. Zhou. Mining coherent dense subgraphs across massive biological networks for functional discovery. ISMB, 2005.
14. D. Jiang, J. Pei, M. Ramanathan, C. Tang, and A. Zhang. Mining coherent gene clusters from gene-sample-time microarray data. In SIGKDD, 2004.
15. R. Karp. Reducibility among combinatorial problems. In Proceedings of a Symposium on the Complexity of Computer Computations, 1972.
16. D. Lockhart and et al. Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat. Biotechnol., 14:1675-80, 1996.
17. M. Middendorf, A. Kundaje, C. Wiggins, Y. Freund, and C. Leslie. Predicting genetic regulatory response using classification. In Proc. of ISMB, 2004.
18. J. Moon and L. Moser. On cliques in graphs. Israel Journal of Mathematics, 3:23-28, 1965.
19. R. Osteen. Clique detection algorithms based on line addition and line removal. SIAM Journal on Applied Mathematics, 26(1): 126-35, 1974.
20. M. Schena, D. Shalon, R. Davis, and P. Brown. Quantitative monitoring of gene expression patterns with a complementary dna microarray. Science, 270:467-70, 1995.
21. R. Shamir and R. Sharan. Click: A clustering algorithm for gene expression analysis. In ISMB, 2000.
22. V. Stix. Finding all maximal cliques in dynamic graphs. Computational Optimization Appl., 27(2):173-86, 2004.
23. P. Tamayo and et al. Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation. In Proc. Natl. Acad. Sci. USA, volume 96, pages 2907-12, 1999.
24. A. Tanay, R. Sharan, M. Kupiec, and R. Shamir. Revealing modularity and organization in the yeast molecular network by integrated analysis of highly heterogeneous genomewide data. Proc. Natl. Acad. Sci. USA, 101(9):2981-86, 2004.
25. S. Tavazoie, J. Hughes, M. Campbell, R. Cho, and G. Church. Systematic determination of genetic network architecture. Nat. Genetics, 22:281-85, 1999.
26. V. Velculescu, L. Zhang, B. Vogelstein, and K. Kinzler. Serial analysis of gene expression. Science, 270, 1995.
27. H. Wang, W Wang, J. Yang, and P. Yu. Clustering by pattern similarity in large data sets. In SIGMOD, 2002.
28. X. Xu, Y. Lu, A. Tung, and W. Wang. Mining shifting-and-scaling co-regulation patterns on gene expression profiles. In ICDE, 2006.
29. L. Zhang and M. Zaki. Tricluster: An effective algorithm for mining coherent clusters in 3d microarrary data. In SIGMOD, 2005.