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Efficiently Mining Time-Delayed Gene Expression Patterns

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4 Author(s)
Guoren Wang ; School of Information Science and Engineering, and Key Laboratory of Medical Image Computing, Northeastern University , Shenyang, China ; Linjun Yin ; Yuhai Zhao ; Keming Mao

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.

Published in:

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)  (Volume:40 ,  Issue: 2 )