Clustering of gene expression data based on shape similarity

Eurasip Journal on Bioinformatics and Systems Biology

Volumn 2009, Issue , 2009, Pages

  Hestilow, Travis J ^a   Huang, Yufei ^a,b  

^a The University of Texas at San Antonio   (United States)

^b UNIVERSITY OF TEXAS HEALTH SCIENCE CENTER AT SAN ANTONIO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ESCHERICHIA COLI;

EID: 65649108093     PISSN: 16874145     EISSN: 16874153     Source Type: Journal    
DOI: 10.1155/2009/195712     Document Type: Article

References (28)

1. Jiang D., Tang C., Zhang A., Cluster analysis for gene expression data: a survey IEEE Transactions on Knowledge and Data Engineering 2004 16 11 1370 1386
2. Asyali M. H., Colak D., Demirkaya O., Inan M. S., Gene expression profile classification: a review Current Bioinformatics 2006 1 1 55 73
3. Bar-Joseph Z., Gerber G. K., Gifford D. K., Jaakkola T. S., Simon I., Continuous representations of time-series gene expression data Journal of Computational Biology 2003 10 3-4 341 356
4. Ma P., Castillo-Davis C. I., Zhong W., Liu J. S., A data-driven clustering method for time course gene expression data Nucleic Acids Research 2006 34 4 1261 1269
5. Rueda L., Bari A., Ngom A., Clustering time-series gene expression data with unequal time intervals Transactions on Computational Systems Biology X 2008 5410 Berlin, Germany Springer 100 123 Lecture Notes in Computer Science
6. Yuan Y., Li C.-T., Unsupervised clustering of gene expression time series with conditional random fields Proceedings of the Inaugural IEEE International Conference on Digital EcoSystems and Technologies (DEST '07) February 2007 Cairns, Australia 571 576
7. Kim K., Zhang S., Jiang K., Measuring similarities between gene expression profiles through new data transformations BMC Bioinformatics 2007 8, article 29 1 14
8. Wen X., Fuhrman S., Michaels G. S., Large-scale temporal gene expression mapping of central nervous system development Proceedings of the National Academy of Sciences of the United States of America 1998 95 1 334 339
9. Müller-Levet C. S., Klawonn F., Cho K.-H., Yin H., Wolkenhauer O., Clustering of unevenly sampled gene expression time-series data Fuzzy Sets and Systems 2005 152 1 49 66
10. Balasubramaniyan R., Hüllermeier E., Weskamp N., Kämper J., Clustering of gene expression data using a local shape-based similarity measure Bioinformatics 2005 21 7 1069 1077
11. Phang T. L., Neville M. C., Rudolph M., Hunter L., Trajectory clustering: a non-parametric method for grouping gene expression time courses, with applications to mammary development Proceedings of the 8th Pacific Symposium on Biocomputing (PSB '03) January 2003 Lihue, Hawaii, USA 351 362
12. Tjaden B., An approach for clustering gene expression data with error information BMC Bioinformatics 2006 7, article 17 1 15
13. Ben-Hur A., Elisseeff A., Guyon I., A stability based method for discovering structure in clustered data Proceedings of the 7th Pacific Symposium on Biocomputing (PSB '02) January 2002 Lihue, Hawaii, USA 6 17
14. Dimitriadou E., Dolničar S., Weingessel A., An examination of indexes for determining the number of clusters in binary data sets Psychometrika 2002 67 1 137 159
15. Dudoit S., Fridlyand J., A prediction-based resampling method for estimating the number of clusters in a dataset Genome Biology 2002 3 7 1 21
16. Tibshirani R., Walther G., Hastie T., Estimating the number of clusters in a data set via the gap statistic Journal of the Royal Statistical Society. Series B 2001 63 2 411 423
17. Sun H., Sun M., Trail-and-error approach for determining the number of clusters 3930 Proceedings of the 4th International Conference on Machine Learning and Cybernetics (ICMLC '05) August 2006 Guangzhou, China 229 238 Lecture Notes in Computer Science
18. Wild D. L., Rasmussen C. E., Ghahramani Z., A Bayesian approach to modeling uncertainty in gene expression clusters Proceedings of the 3rd International Conference on Systems Biology (ICSB '02) December 2002 Stockholm, Sweden
19. Xu Y., Olman V., Xu D., Minimum spanning trees for gene expression data clustering Genome Informatics 2001 12 24 33
20. Yan M., Ye K., Determining the number of clusters using the weighted gap statistic Biometrics 2007 63 4 1031 1037
21. Beal M. J., Ghahramani Z., The variational Bayesian EM algorithm for incomplete data: with application to scoring graphical model structures 7 Proceedings of the 7th Valencia International Meeting on Bayesian Statistics June 2003 Tenerife, Spain 453 464
22. Rousseeuw P. J., Silhouettes: a graphical aid to the interpretation and validation of cluster analysis Journal of Computational and Applied Mathematics 1987 20 53 65
23. Schwarz G., Estimating the dimension of a model Annals of Statistics 1978 6 2 461 464
24. The University of Oklahoma's E. coli Gene Expression Database, http://chase.ou.edu/oubcf/
25. The Entrez Genome Database. National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Escherichia coli K-12 data. http://www.ncbi.nlm.nih.gov/
26. Shannon P., Markiel A., Ozier O., Cytoscape: a software environment for integrated models of biomolecular interaction networks Genome Research 2003 13 11 2498 2504
27. Maere S., Heymans K., Kuiper M., BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks Bioinformatics 2005 21 16 3448 3449
28. Yuan Y., Li C.-T., Probabilistic framework for gene expression clustering validation based on gene ontology and graph theory Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '08) March-April 2008 Las Vegas, Nev, USA 625 628