Mining Time-Delayed Coherent Patterns in Time Series Gene Expression Data

  • Linjun Yin
  • Guoren Wang
  • Keming Mao
  • Yuhai Zhao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4093)


Unlike previous pattern-based biclustering methods that focus on grouping objects on the same subset of dimensions, in this paper, we propose a novel model of coherent cluster for time series gene expression data, namely td-cluster (time-delayed cluster). Under this model, objects can be coherent on 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 the gene regulatory networks. This work is missed by previous research. A novel algorithm is also presented and implemented to mine all the significant td-clusters. Experimental results from both real and synthetic microarray datasets prove its effectiveness and efficiency.


Time Sequence Gene Regulatory Network Slide Window Approach Pruning Rule Scaling Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Linjun Yin
    • 1
  • Guoren Wang
    • 1
  • Keming Mao
    • 1
  • Yuhai Zhao
    • 1
  1. 1.Northeastern UniversityChina

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