Identifying gene regulatory networks from experimental data
RECOMB '99 Proceedings of the third annual international conference on Computational molecular biology
Finding generalized projected clusters in high dimensional spaces
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Analysis techniques for microarray time-series data
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Clustering by pattern similarity in large data sets
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Efficient Retrieval of Similar Time Sequences Under Time Warping
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Time-frequency feature detection for time-course microarray data
Proceedings of the 2004 ACM symposium on Applied computing
A Time Series Analysis of Microarray Data
BIBE '04 Proceedings of the 4th IEEE Symposium on Bioinformatics and Bioengineering
Gene Ontology Friendly Biclustering of Expression Profiles
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
TRICLUSTER: an effective algorithm for mining coherent clusters in 3D microarray data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Analyzing time series gene expression data
Bioinformatics
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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.