Name
Abstract | ||
|---|---|---|
Clustering still represents the most commonly used technique to analyze gene expression data—be it classical clustering approaches that aim at finding biologically relevant gene groups or biclustering methods that focus on identifying subset of genes that behave similarly over a subset of conditions. Usually, the measurements of different experiments are mixed together in a single gene expression matrix, where the information about which experiments belong together, e.g., in the context of a time course, is lost. This paper investigates the question of how to exploit the information about related experiments and to effectively use it in the clustering process. To this end, the idea of order preserving clusters that has been presented in [2] is extended and integrated in an evolutionary algorithm framework that allows simultaneous clustering over multiple time course experiments while keeping the distinct time series data separate. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/978-3-540-32003-6_4 | EvoWorkshops |
Keywords | Field | DocType |
clustering process,time course,biclustering method,biologically relevant gene group,multiple time course experiment,gene expression data,classical clustering approach,distinct time series data,simultaneous clustering,single gene expression matrix,evolutionary algorithm,gene expression,time series data | Data mining,Fuzzy clustering,Clustering high-dimensional data,Evolutionary algorithm,Correlation clustering,Computer science,Algorithm,Constrained clustering,Biclustering,Cluster analysis,Genetic algorithm,Distributed computing | Conference |
Volume | ISSN | ISBN |
3449 | 0302-9743 | 3-540-25396-3 |
Citations | PageRank | References |
13 | 0.88 | 9 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| a stefan bleuler a | 1 | 795 | 35.85 |
| Eckart Zitzler | 2 | 4678 | 291.01 |