Clustering by pattern similarity in large data sets
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
MaPle: A Fast Algorithm for Maximal Pattern-based Clustering
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Interactive exploration of coherent patterns in time-series gene expression data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
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
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The complexity of a biological system provides a great diversity of correlations among genes/gene clusters, including synchronous and asynchronous co-regulations, each of which can be further divided into two categories: activation and inhibition. Most existing methods can only identify the synchronous activation patterns, such as shifting, scaling and shifting-and-scaling, however, few focuses on capturing both synchronous and asynchronous co-regulations. In this paper, we propose a coding scheme, where two genes with the same code must be co-regulated. Based on the coding scheme, an efficient clustering algorithm is devised to simultaneously capture all known co-regulated relationships (synchronous and asynchronous) among genes/gene clusters. Furthermore, the detailed and complete co-regulation information, which facilitates the study of genetic regulatory networks, can be easily derived from the resulting clusters. Experiments from both real and synthetic microarray datasets prove the effectiveness and efficiency of our method.