Divisive correlation clustering algorithm (DCCA) for grouping of genes: Detecting varying patterns in expression profiles

Bioinformatics

Volumn 24, Issue 11, 2008, Pages 1359-1366

  Bhattacharya, Anindya ^a   De, Rajat K ^b  

^a NETAJI SUBHASH ENGINEERING COLLEGE   (India)

^b INDIAN STATISTICAL INSTITUTE   (India)

Author keywords

[No Author keywords available]

Indexed keywords

ADENOSINE TRIPHOSPHATE; RIBONUCLEOPROTEIN;

ARTICLE; CLUSTER ANALYSIS; DATA ANALYSIS SOFTWARE; GENE CLUSTER; GENE EXPRESSION PROFILING; GENETIC ALGORITHM; GENETIC DATABASE; NONHUMAN; PRIORITY JOURNAL; RIBOSOME; YEAST;

EID: 44349106021     PISSN: 13674803     EISSN: 13674811     Source Type: Journal    
DOI: 10.1093/bioinformatics/btn133     Document Type: Article

References (36)

1. Alexe,G. et al. (2002) Consensus algorithms for the generation of all maximal bicliques. In Technical Report TF-DIMACS.
2. Alon,N. et al. (2005) Quadratic forms on graphs. In 37th ACM Symposium on Theory of Computing (STOC).
3. Asnburner,M. et al. (2000) Tool for the unification of biology. The gene ontology consortium. Nat. Genet., 25, 25-29.
4. Bansal,N. et al. (2004) Correlation clustering. Mach. Learn., Special Issue, 56, 89-113.
5. Ben-Dor,A. et al. (2002) Discovering local structure in gene expression data: The order-preserving submatrix problem. Proceedings of the Sixth International Conference on Computational Biology (RECOMB'02), pp. 49-57.
6. Berriz,F.G. et al. (2003) Characterizing gene sets with funcassociate. Bioinformatics, 19, 2502-2504.
7. Bezdek,J.C. (1981) Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York.
8. Bezdek,J.C. et al. (1984) FCM: Fuzzy c-means algorithm. Comput. Geosci, 10, 191-203.
9. Charikar,M and Wirth,A. (2004) MnXiMi7ing quadratic programs: Extending grothendieck's inequality. In Proceedings of the 45th IEEE Symposium on Foundations of Computer Science (FOCS), pp. 524-533.
10. Charikar,M. et al. (2003) Clustering with qualitative information. In Proceedings of the 44th IEEE Symposium on Foundations of Computer Science (FOCS), pp. 524-533.
11. Cheng,Y. and Churh,G.M. (2000) Biclustering of expression data. Proc. Int. Conf. Intell. Syst. Mol. Biol., 8, 93-103.
12. Cho, R.J. et al. (1998) A genome-wide, transcriptional analysis of the mitotic cell cycle. Mol. Cell, 2, 65-73.
13. Cohen,W. and Richman, J. (2002) Learning to match and cluster large, high-dimensional data sets for data integration. In Eigth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD).
14. Demaine,E.D. and Immorlica,N. (2003) Correlation clustering with partial information. In Proceedings of the 6th International Workshop on Approximantion Aigorithms for Combinanatorial Opitimization Problems and 7th International Workshop on Randomization and Approximation Techniques in Computer Science (RANDOM-APPROX 2003). Princeton, New Jersey, pp. 1-13.
15. Demaine,E.D. et al. (2006) Correlation clustering in general weighted graphs. Theoret. Comput. Sci. 361, 172-187.
16. Dembele,D. and Kastner,P. (2003) Fuzzy c-means method for clustering microarray data. Bioinformatics, 19, 973-980.
17. Dunn,J.C. (1973) A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters. J. Cybernet, 3, 32-57.
18. Cietz,G. et al. (2000) Coupled two-way clustering analysis of gene microarray data. Proc. Natl Acad Sci USA, 12079-12084.
19. Gibbons,F. and Roth,F. (2002) Judging the quality of gene expression-based clustering methods using gene annotation. Genome Res. 12, 1574-1581.
20. Gustafson,E.E. and Kessel,W.C. (1979) Fuzzy clustering with a fuzzy covarience matrix. In Proceedings of the IEEE CDC. San Diego, California, pp. 761-766.
21. Han,J. and Kamber,M. (2001) Data Mining: Concepts and Techniques. Morgan Kaufmann, CA, USA.
22. Ihmels,J. et al. (2004) Defining transcription modules using large-scale gene expression data. Bioinformatics, 20, 1993-2003.
23. Issel-Tarver,L. et al. (2002) Saccharomyces genome database. Methods Enzymol, 350, 329-346.
24. Jain,A.K. and Dubes,R.C. (1988) Algorithms for Clustering Data. Prentice Hall, New Jersey.
25. Kim,D.W. et al. (2005) Detecting clusters of different geometrical shapes in microarray gene expression data. Bioinformatics, 21 1927-1934.
26. Kim,P.M. and Tidor,B. (2003) Subsystem identification through dimensionality reduction of large-scale gene expression data. Genome Res., 13, 1706-1718.
27. Kluger,Y. et al. (2003) Spectral biclustering of microarray cancer data: Co-clustering genes and conditions. Genome Res., 703-716.
28. Lukashin,A.V. and Fuchs,R. (2001) Analysis of temporal gene expression profiles:cLustering by simulated annealing and determining the optimal number of clusters. Bioinformatics, 17, 405-414.
29. Mitra,S. and Acharya,T. (2003) Data Mining: Multimedia, Soft Computing, and Bioinformatics. John Wiley, New York.
30. Murali,T.M. and Kasif,S. (2003) Extracting conserved gene expression motifs from gene expression data. In Proceeding of the Pacific Symposium on Biovomputing, pp. 77-88.
31. Prelic,A. et al. (2006) A systematic comparison and evaluation of biclustering methods for gene expression data. Bioinformatics, 22, 1122-1129.
32. Press,W. et al. (2003) Numerical Recipies - The Art of Scientific Computing. Cambridge Uinversity Press, Cambridge.
33. Qin. J. et al. (2003) Kernel hierarchical gene clustering from microarray expression data. Bioinformatics, 19, 2097-2104.
34. Sharan,R. et al. (2003) Click and expander: A system for clustering and visualizing gene expression data. Bioinformatics, 19, 1787-1799.
35. Tanay,A. et al. (2002) Discovering statistically significant biclusters in gene expression data. Bioinformatics, S136-S144.
36. Xu, Y. et al. (2002) Clustering gene, expression data using a graph-theoretic approach: An application of minmum spanning trees. Bioinformatics, 18, 536-545.