JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Clustering of Time-Course Microarray Data Using Pharmacokinetic Parameter
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Clustering of Time-Course Microarray Data Using Pharmacokinetic Parameter
Lee, Hyo-Jung; Kim, Peol-A; Park, Mi-Ra;
  PDF(new window)
 Abstract
A major goal of time-course microarray data analysis is the detection of groups of genes that manifest similar expression patterns over time. The corresponding numerous cluster algorithms for clustering time-course microarray data have been developed. In this study, we proposed a clustering method based on the primary pharmacokinetic parameters in the pharmacokinetics study for assessment of pharmaceutical equivalents between two drug products. A real data and a simulation data was used to demonstrate the usefulness of the proposed method.
 Keywords
Time-course microarray data;pharmacokinetic parameter;clustering;
 Language
Korean
 Cited by
 References
1.
Chu, S., DeRisi, J., Eisen, M., Mulholland, J., Botstein, D., Brown, P. O. and Herskowitz, I. (1998). The transcriptional program of sporulation in budding yeast, Science, 282, 699-705. crossref(new window)

2.
Hoon, D., Imoto, S. and Miyano, S. (2002). Statistical analysis of a small set of time-ordered gene expression data using linear splines, Bioinformatics, 18, 1477-1485. crossref(new window)

3.
Hubert, L. and Arabie, P. (1985). Comparing partitions, Journal of the Classification, 2, 193-218. crossref(new window)

4.
Lobenhofer, E. K., Bennett, L., Cable, P. L., Li, L., Bushel, P. R. and Afshari, C. A. (2002). Regulation of dna replication fork genes by 17beta-estradiol, Molecular Endocrinology, 16, 1215-1229. crossref(new window)

5.
Luan, Y. and Li, H. (2003). Clustering of time-course gene expression data using a mixed-effects model with B-splines, Bioinformatics, 19, 474-482. crossref(new window)

6.
Peddada, S. D., Lobenhofer, E. K., Li, L., Afshari, C. A., Weinberg, C. R. and Umbach, D. M. (2003). Gene selection and clustering for time-course and dose-response microarray experiments using order-restricted inference, Bioinformatics, 19, 834-841. crossref(new window)

7.
Rand, W. M. (1971). Objective criteria for the evaluation of clustering methods, Journal of American Statistical Association, 66, 846-850. crossref(new window)

8.
Schliep, A., Schonhuth, A. and Steinhoff, C. (2003). Using hidden Markov models to analyze gene expression time course data, Bioinformatics Supplement, 19, i255-263. crossref(new window)

9.
Song, J. J., Lee, H. J., Morris, J. S. and Kang, S. (2007). Clustering of time-course gene expression data using functional data analysis, Computational Biology and Chemistry, 31, 265-274. crossref(new window)

10.
Spellman, P. T., Sherlock, G., Zhang, M. Q., Iyer, V. R., Anders, K., Eisen, M. B., Brown, P. O., Botstein, D. and Futcher, B. (1998). Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization, Molecular Biology of the Cell, 12, 3273-3297.

11.
Tibshirani, R. J., Hastie, T. J., Narasimhan, B. and Chu, G. (2002). Diagnosis of multiple cancer types by shrunken centroids of gene expression, Proceedings of the National Academy of Sciences, 99, 6567-6572. crossref(new window)

12.
Yeung, K. Y., Fraley, C., Murua, A., Raftery, A. E. and Raftery, W. L. (2001). Model-based clustering and data transformations for gene expression data, Bioinformatics, 17, 977-987. crossref(new window)

13.
Yi, S.-G, Joo, Y.-J. and Park, T. (2009). Rank-based clustering analysis for the time-course microarray data, Journal of Bioinformatics and Computational Biology, 7, 75-91. crossref(new window)