A biclustering method to discover co-regulated genes using diverse gene expression datasets

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

Volumn 5462 LNBI, Issue , 2009, Pages 151-163

  Bozdaǧ, Doruk ^a   Parvin, Jeffrey D ^a   Catalyurek, Umit V ^a  

^a OHIO STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BICLUSTERING; BICLUSTERING ALGORITHM; BICLUSTERS; BIOLOGICAL PATHWAYS; BIOLOGICAL PROCESS; BREAST CANCER; BREAST TUMOR; CO-REGULATED GENES; CO-REGULATION; COMPLEX DATASETS; COMPLEX PROCESSES; CRITICAL FUNCTIONS; DATA SETS; DNA REPAIR; FALSE NEGATIVES; GENE EXPRESSION ANALYSIS; GENE EXPRESSION DATASETS; GENE FUNCTION; LARGE DATASETS; MICROARRAY DATA SETS; MITOTIC SPINDLE; PEARSON CORRELATION COEFFICIENTS; RELATED FUNCTIONS; TUMOR SUPPRESSORS;

BIOACTIVITY; BIOLOGY; DATA REDUCTION; DNA; GENE EXPRESSION; MINING; NUCLEIC ACIDS; PROTEINS; TUMORS;

BIOINFORMATICS;

EID: 68249119165     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-00727-9_16     Document Type: Conference Paper

References (16)

1. Agrawal, H.: Extreme self-organization in networks constructed from gene expression data. Phys. Rev. Lett. 89(26), 268702 (2002)
2. Ben-Dor, A., Chor, B., Karp, R., Yakhini, Z.: Discovering local structure in gene expression data: The order-preserving submatrix problem. Int'l Conf. Comput. Biol., 49-57 (2002)
3. Jiang, D., Pei, J., Zhang, A.: DHC: A density-based hierarchical clustering method for time series gene expression data. In: IEEE Symp. Bioinform. and Bioeng., pp. 393-400 (2003)
4. Madeira, S.C., Oliveira, A.L.: Biclustering algorithms for biological data analysis: A survey. IEEE/ACMTrans. Comput. Biology Bioinform. 1(1), 24-45 (2004)
5. Pujana, M.A., et al.: Network modeling links breast cancer susceptibility and centrosome dysfunction. Nature Genetics 39(11), 1338-1349 (2007)
6. Devore, J.L.: Probability and Statistics for Engineeringand Sciences. Brook/Cole Publishing Company (1991)
7. Edgar, R., Domrachev, M., Lash, A.E.: Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucl. Acids Res. 30(1), 207-210 (2002)
8. Cheng, Y., Church, G.M.: Biclustering of expression data. Int'l Conf. Intelligent Systems for Molecular Biology, 93-103 (2000)
9. Segal, E., Taskar, B., Gasch, A., Friedman, N., Koller, D.: Rich probabilistic models for gene expression. Bioinformatics 17(suppl. 1), S243-S252 (2001)
10. Wang, H.,Wang,W., Yang, J,Yu, P.S.: Clustering by pattern similarity in large data sets. In: ACM SIGMOD (2002)
11. Liu, J., Wang, W.: Op-cluster: Clustering by tendency in high dimensional space. In: IEEE Int'l. Conf. Data Mining, p. 187 (2003)
12. Murali, T., Kasif, S.: Extracting conserved gene expression motifs from gene expression data. In: Pac. Symp. Biocomp., vol.8 (2003)
13. Tanay, A., Sharan, R., Shamir, R.: Discovering statistically significant biclusters in gene expression data. Bioinformatics 18(suppl. 1), 136-144 (2002)
14. Kluger, Y., Basri, R., Chang, J.T., Gerstein, M.: Spectral biclustering of microarray data: coclustering genes and conditions. Genome Res. 13(4), 703-716 (2003)
15. Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Analysis and Machine Intelligence 24(5), 603-619 (2002)
16. Casella, G., Wells, M.T.: Is Pitman closeness a reasonable criterion?: Comment. Journal of the American Statistical Association 88(421), 70-71 (1993)