Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICDE.2006.98
Title: Mining shifting-and-scaling Co-regulation patterns on gene expression profiles
Authors: Xu, X. 
Lu, Y.
Tung, A.K.H. 
Wang, W.
Issue Date: 2006
Source: Xu, X.,Lu, Y.,Tung, A.K.H.,Wang, W. (2006). Mining shifting-and-scaling Co-regulation patterns on gene expression profiles. Proceedings - International Conference on Data Engineering 2006 : 89-. ScholarBank@NUS Repository. https://doi.org/10.1109/ICDE.2006.98
Abstract: 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 pattern-based biclustering algorithm for identifying shifting-and-scaling 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. © 2006 IEEE.
Source Title: Proceedings - International Conference on Data Engineering
URI: http://scholarbank.nus.edu.sg/handle/10635/43208
ISBN: 0769525709
ISSN: 10844627
DOI: 10.1109/ICDE.2006.98
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