An effective density-based hierarchical clustering technique to identify coherent patterns from gene expression data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6634 LNAI, Issue PART 1, 2011, Pages 225-236

  Sarmah, Sauravjyoti ^a   Sarmah, Rosy Das ^a   Bhattacharyya, Dhruba Kumar ^a  

^a TEZPUR UNIVERSITY   (India)

Author keywords

coherent patterns; Maximal space cluster; p value; reduced space cluster; z score

Indexed keywords

DATA MINING; GENE EXPRESSION; TREES (MATHEMATICS);

COHERENT PATTERNS; MAXIMAL SPACE CLUSTER; P-VALUES; REDUCED SPACE; Z-SCORES;

HIERARCHICAL CLUSTERING;

EID: 79957940909     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-20841-6_19     Document Type: Conference Paper

References (30)

1. Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, San Fransisco (2004)
2. Stekel, D.: Microarray Bioinformatics. Cambridge University Press, Cambridge (2006)
3. Ben-Dor, A., Shamir, R., Yakhini, Z.: Clustering gene expression patterns. Journal of Computational Biology 6(3-4), 281-297 (1999)
4. Eisen, M., Spellman, P., Brown, P., Botstein, D.: Cluster analysis and display of genome-wide expression patterns. Proc. of National Academy of Sciences 95, 14863-14868 (1998)
5. Jiang, D., Tang, C., Zhang, A.: Cluster analysis for gene expression data: A survey (2003), www.cse.buffalo.edu/DBGROUP/bioinformatics/papers/survey. pdf
6. Jiang, D., Pei, J., Zhang, A.: DHC: a density-based hierarchical clustering method for time series gene expression data. In: Proc. of BIBE, Bethesda, Maryland, p. 393 (2003)
7. McQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Proc. of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281-297 (1967)
8. Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)
9. Heyer, L., Kruglyak, S., Yooseph, S.: Exploring expression data: identication and analysis of co-expressed genes. Genome Research 9, 1102-1115 (1999)
10. Tamayo, P., Slonim, D., Mesirov, J., et al.: Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation. Proc. of National Academy of Sciences 96(6), 2907-2912 (1999)
11. Dopazo, J., Carazo, J.M.: Phylogenetic reconstruction using an unsupervised neural network that adopts the topology of a phylogenetic tree. Journal of Molecular Evolution 44, 226-233 (1997)
12. Bhattacharya, A., De, R.: Divisive correlation clustering algorithm (DCCA) for grouping of genes: detecting varying patterns in expression profiles. Bioinformatics 24(11), 1359-1366 (2008)
13. Luo, F., Khan, L., Bastani, F., et al.: A dynamically growing self-organizing tree (DGSOT) for hierarchical clustering gene expression profiles. Bioinformatics 20(16), 2605-2617 (2004)
14. Shu, G., Zeng, B., Chen, Y.P., Smith, O.H.: Performance assessment of kernel density clustering for gene expression profile data. Comparative and Functional Genomics 4, 287-299 (2003)
15. Das, R., Bhattacharyya, D.K., Kalita, J.K.: Clustering gene expression data using a regulation based density clustering. IJRTE 2(1-6), 76-78 (2009)
16. Das, R., Bhattacharyya, D.K., Kalita, J.K.: Clustering gene expression data using an effective dissimilarity measure. IJCB (Special Issue) 1(1), 55-68 (2010)
17. Jarvis, R.A., Patrick, E.A.: Clustering using a similarity measure based on shared nearest neighbors. IEEE Transactions on Computers 11 (1973)
18. Herrero, J., Valencia, A., Dopazo, J.: A hierarchical unsupervised growing neural network for clustering gene expression patterns. Bioinformatics 17, 126-136 (2001)
19. Sharan, R., Shamir, R.: CLICK: A clustering algorithm with applications to gene expression analysis. In: Proc. of ISMB. AAAI Press, Menlo Park (2000)
20. Bellaachia, A., Portnoy, D., Chen, A.G., Elkahloun, Y.: E-CAST: A data mining algorithm for gene expression data. In: Proc. of the BIOKDD, vol. 49 (2002)
21. Das, R., Kalita, J.K., Bhattacharyya, D.K.: A new approach for clustering gene expression time series data. IJBRA 5(3), 310-328 (2009)
22. Priyadarshini, G., Chakraborty, B., Das, R., et al.: Highly coherent pattern identification using graph-based clustering. In: Proc. of BIOT, Lafayette, Louisiana, US, pp. 29-38 (2010)
23. Bandyopadhyay, S., Mukhopadhyay, A., Maulik, U.: An improved algorithm for clustering gene expression data. Bioinformatics 23(21), 2859-2865 (2007)
24. Maulik, U., Mukhopadhyay, A., Bandyopadhyay, S.: Combining pareto-optimal clusters using supervised learning for identifying co-expressed genes. BMC Bioinformatics 10(27) (2009)
25. Yun, T., Hwang, T., Cha, K., et al.: CLIC: clustering analysis of large microarray datasets with individual dimension-based clustering. Nucleic Acids Research 38, W246-W253 (2010)
26. Cho, R.J., Campbell, M., Winzeler, E., et al.: A genome-wide transcriptional analysis of the mitotic cell cycle. Molecular Cell 2(1), 65-73 (1998)
27. DeRisi, J.L., Iyer, V.R., Brown, P.O.: Exploring the metabolic and genetic control of gene expression on a genomic scale. Science 278, 680-686 (1997)
28. Iyer, V.R., Eisen, M.B., Ross, D.T., et al.: The transcriptional program in the response of the human fibroblasts to serum. Science 283, 83-87 (1999)
29. Gibbons, F., Roth, F.: Judging the quality of gene expression based clustering methods using gene annotation. Genome Research 12, 1574-1581 (2002)
30. Berriz, F.G., et al.: Characterizing gene sets with funcassociate. Bioinformatics 19, 2502-2504 (2003)