Abstract
Unlike pattern-based biclustering methods that focus on grouping objects in the same subset of dimensions, in this paper, we propose a novel model of coherent clustering for time-series gene expression data, i.e., time-delayed cluster (td-cluster). Under this model, objects can be coherent in different subsets of dimensions if these objects follow a certain time-delayed relationship. Such a cluster can discover the cycle time of gene expression, which is essential in revealing gene regulatory networks. This paper is the first attempt to mine time-delayed gene expression patterns from microarray data. A novel algorithm is also presented and implemented to mine all significant td-clusters. Our experimental results show following two results: 1) the td-cluster algorithm can detect a significant amount of clusters that were missed by previous models, and these clusters are potentially of high biological significance and 2) the td-cluster model and algorithm can easily be extended to 3-D gene × sample × time data sets to identify 3-D td-clusters.
Original language | English |
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Article number | 5299237 |
Pages (from-to) | 400-411 |
Number of pages | 12 |
Journal | IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics |
Volume | 40 |
Issue number | 2 |
DOIs | |
Publication status | Published - Apr 2010 |
Externally published | Yes |
Keywords
- Gene expression
- Gene expression patterns
- Microarray
- Subspace clustering
- Time delayed