Efficiently mining time-delayed gene expression patterns

Guoren Wang*, Linjun Yin, Yuhai Zhao, Keming Mao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)

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 languageEnglish
Article number5299237
Pages (from-to)400-411
Number of pages12
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume40
Issue number2
DOIs
Publication statusPublished - Apr 2010
Externally publishedYes

Keywords

  • Gene expression
  • Gene expression patterns
  • Microarray
  • Subspace clustering
  • Time delayed

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