Clustering time-series gene expression data with unequal time intervals

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

Volumn 5410 LNBI, Issue , 2008, Pages 100-123

  Rueda, Luis ^a   Bari, Ataul ^a   Ngom, Alioune ^a  

^a UNIVERSITY OF WINDSOR   (Canada)

Author keywords

Clustering; Gene expression; Microarrays; Time series data

Indexed keywords

BIOASSAY; CLASSIFICATION (OF INFORMATION); CLUSTER ANALYSIS; GENE EXPRESSION; MICROARRAYS; TIME SERIES; YEAST;

AGGLOMERATIVE CLUSTERING; CLASSIFICATION ACCURACY; CLUSTERING; CLUSTERING METHODS; CLUSTERING TIME SERIES; GENE EXPRESSION DATA; TEMPORAL EXPRESSIONS; TIME-SERIES DATA;

CLUSTERING ALGORITHMS;

EID: 59149100683     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-92273-5_6     Document Type: Conference Paper

References (32)

1. Bari, A., Rueda, L.: A New Profile Alignment Method for Clustering Gene Expression Data. In: Lamontagne, L., Marchand, M. (eds.) Canadian AI 2006. LNCS, vol. 4013, pp. 86-97. Springer, Heidelberg (2006)
2. Bréhélin, L.: Clustering Gene Expression Series with Prior Knowledge. In: Casadio, R., Myers, G. (eds.) WABI 2005. LNCS (LNBI), vol. 3692, pp. 27-38. Springer, Heidelberg (2005)
3. Cho, R.J., Campbell, M.J., Winzeler, E.A., Steinmetz, L., Conway, A.,Wodicka, L., Wolfsberg, T.G., Gabrielian, A.E., Landsman, D., Lockhart, D.J., Davis, R.W.: A genome-wide transcriptional analysis of the mitotic cell cycle. Molecular Cell 2(1), 65-73 (1998)
4. Conesa, A., Nueda, M.J., Ferrer, A., Talon, M.: maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments. Bioinformatics 22(9), 1096-1102 (2006)
5. Chu, S., DeRisi, J., Eisen, M., Mulholland, J., Botstein, D., Brown, P., Herskowitz, I.: The transcriptional program of sporulation in budding yeast. Science 282, 699- 705 (1998)
6. Déjean, S., Martin, P.G.P., Baccini, A., Besse, P.: Clustering Time-Series Gene Expression Data Using Smoothing Spline Derivatives. EURASIP J. Bioinform. Syst. Biol. 2007, 70561 (2007)
7. Drǎghici, S.: Data Analysis Tools for DNA Microarrays. Chapman & Hall, Boca Raton (2003)
8. Eisen, M., Spellman, P., Brown, P., Botstein, D.: Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. 95, 14863-14868 (1998)
9. Ernst, J., Nau, G.J., Bar-Joseph, Z.: Clustering Short Time Series Gene Expression Data. Bioinformatics 21(suppl. 1), i159-i168 (2005)
10. Gasch, A.P., Eisen, M.B.: Exploring the conditional coregulation of yeast gene expression through fuzzy k-means clustering. Genome Biology 3(11), 0059.1-0059.22 (2002)
11. Guillemin, K., Salama, N., Tompkins, L., Falkow, S.: Cag pathogenicity islandspecific responses of gastric epithelial cells to Helicobacter pylori infection. Proc. Natl. Acad. Sci. 99, 15136-15141 (2002)
12. Hartigan, J.A.: Clustering Algorithms. John Wiley and Sons, Chichester (1975)
13. Heijne, W.H., Stierum, R.H., Slijper, M., van Bladeren, P.J., van Ommen, B.: Toxicogenomics of bromobenzene hepatotoxicity: a combined transcriptomics and proteomics approach. Biochem. Pharmacol. 65, 857-875 (2003)
14. Heyer, L., Kruglyak, S., Yooseph, S.: Exploring expression data: identification and analysis of coexpressed genes. Genome Res. 9, 1106-1115 (1999)
15. Hogg, R., Craig, A.: Introduction to Mathematical Statistics, 5th edn. MacMillan, Basingstoke (1995)
16. Hwang, J., Peddada, S.: Confidence interval estimation subject to order restrictions. Ann. Statist. 22, 67-93 (1994)
17. Iyer, V., Eisen, M., Ross, D., Schuler, G., Moore, T., Lee, J., Trent, J., Staudt, L., Hudson Jr., J., Boguski, M.: The transcriptional program in the response of human fibroblasts to serum. Science 283, 83-87 (1999)
18. Bar-Joseph, Z., Gerber, G., Jaakkola, T., Gifford, D., Simon, I.: Continuous representations of time series gene expression data. Journal of Computational Biology 10(3-4), 341-356 (2003)
19. Lobenhofer, E., Bennett, L., Cable, P., Li, L., Bushel, P., Afshari, C.: Regulation of DNA replication fork genes by 17betaestradiol. Molec. Endocrin. 16, 1215-1229 (2002)
20. Maulik, U., Bandyopadhyay, S.: Performance Evaluation of Some Clustering Algorithms and Validity Indices. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(12), 1650-1654 (2002)
21. Moller-Levet, C., Klawonn, F., Cho, K.-H., Wolkenhauer, O.: Clustering of unevenly sampled gene expression time-series data. Fuzzy sets and Systems 152(1,16), 49-66 (2005)
22. Peddada, S., Prescott, K., Conaway, M.: Tests for order restrictions in binary data. Biometrics 57, 1219-1227 (2001)
23. Peddada, S., Lobenhofer, E., Li, L., Afshari, C., Weinberg, C., Umbach, D.: Gene selection and clustering for time-course and dose-response microarray experiments using order-restricted inference. Bioinformatics 19(7), 834-841 (2003)
24. Petrie, T.: Probabilistic functions of finite state Markov chains. Ann. Math. Statist. 40, 97-115 (1969)
25. Ramoni, M., Sebastiani, P., Kohane, I.: Cluster analysis of gene expression dynamics. Proc. Natl. Acad. Sci. USA 99(14), 9121-9126 (2002)
26. Ramsay, J., Silverman, B.: Functional Data Analysis, 2nd edn. Springer, New York (2005)
27. Rueda, L., Bari, A.: Clustering Temporal Gene Expression Data with Unequal Time Intervals. In: 2nd International Conference on Bio-Inspired Models of Network, Information, and Computing Systems, Bioinformatics Track, Budapest, Hungary (2007) ICST 978-963-9799-11-0
28. Schliep, A., Schonhuth, A., Steinhoff, C.: Using hidden Markov models to analyze gene expression time course data. Bioinformatics 19, I264-I272 (2003)
29. Spellman, P.T., Sherlock, G., Zhang, M.Q., Iyer, V.R., Anders, K., Eisen, M.B., Brown, P.O., Botstein, D., Futcher, B.: Comprehensive identification of cell cycleregulated genes of the yeast saccharomyces cerevisiae by microarray hybridization. Mol. Biol. Cell. 9, 3273-3297 (1998)
30. Tamayo, P., Slonim, D., Mesirov, J., Zhu, Q., Kitareewan, S., Dmitrovsky, E., Lander, E., Golub, T.: Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation. Proc. Natl. Acad. Sci. 96(6), 2907-2912 (1999)
31. Tavazoie, S., Hughes, J., Campbell, M., Cho, R., Church, G.: Systematic determination of genetic network architecture. Nat. Genet. 22, 281-285 (1999)
32. Zhu1, G., Spellman, P.T., Volpe, T., Brown, P.O., Botstein, D., Davis, T.N., Futcher, B.: Two yeast forkhead genes regulate cell cycle and pseudohyphal growth. Nature 406, 90-94 (2000)