Mining coherent patterns from heterogeneous microarray data
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Maximal Subspace Coregulated Gene Clustering
IEEE Transactions on Knowledge and Data Engineering
CRD: fast co-clustering on large datasets utilizing sampling-based matrix decomposition
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
A scalable framework for discovering coherent co-clusters in noisy data
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Negative correlations in collaboration: concepts and algorithms
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Measuring the quality of shifting and scaling patterns in biclusters
PRIB'10 Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics
Noise-robust algorithm for identifying functionally associated biclusters from gene expression data
Information Sciences: an International Journal
An effective measure for assessing the quality of biclusters
Computers in Biology and Medicine
Parallelized Evolutionary Learning for Detection of Biclusters in Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A unified adaptive co-identification framework for high-d expression data
PRIB'12 Proceedings of the 7th IAPR international conference on Pattern Recognition in Bioinformatics
A survey on enhanced subspace clustering
Data Mining and Knowledge Discovery
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In this paper, we propose a new model for coherent clustering of gene expression data called reg-cluster. The proposed model allows (1) the expression profiles of genes in a cluster to follow any shifting-and-scaling patterns in subspace, where the scaling can be either positive or negative, and (2) the expression value changes across any two conditions of the cluster to be significant. No previous work measures up to the task that we have set: the density-based subspace clustering algorithms require genes to have similar expression levels to each other in subspace; the pattern-based biclustering algorithms only allow pure shifting or pure scaling patterns; and the tendency-based biclustering algorithms have no coherence guarantees. We also develop a novel patternbased biclustering algorithm for identifying shifting-andscaling co-regulation patterns, satisfying both coherence constraint and regulation constraint. Our experimental results show that the reg-cluster algorithm is able to detect a significant amount of clusters missed by previous models, and these clusters are potentially of high biological significance.