TY - GEN
T1 - An efficient algorithm for mining coherent patterns from heterogeneous microarrays
AU - Zhang, Xiang
AU - Wang, Wei
N1 - Copyright:
Copyright 2008 Elsevier B.V., All rights reserved.
PY - 2007
Y1 - 2007
N2 - DNA microarray techniques present a novel way for geneticists to monitor interactions among tens of thousands of genes simultaneously, and have become standard lab routines in gene discovery, disease diagnosis, and drug design. There has been extensive research on coherent subspace clustering of gene expressions measured under consistent experimental settings. This implies that all experiments are run using the same batch of microarray chips with similar characteristics of noise. Algorithms developed under this assumption may not be applicable for analyzing data collected from heterogeneous settings, where the set of genes being monitored may be different and expression levels may be not directly comparable even for the same gene. In this paper, we propose a model, F-cluster, for mining subspace coherent patterns from heterogeneous gene expression data, which is shown effective for revealing truthful patterns and reducing spurious ones. We also develop an efficient and scalable hybrid approach that combines gene-pair based and sample-pair based pruning to generate F-clusters from multiple gene expression matrices simultaneously. The experimental results demonstrate that our model can discover significant clusters that may not be identified by previous models.
AB - DNA microarray techniques present a novel way for geneticists to monitor interactions among tens of thousands of genes simultaneously, and have become standard lab routines in gene discovery, disease diagnosis, and drug design. There has been extensive research on coherent subspace clustering of gene expressions measured under consistent experimental settings. This implies that all experiments are run using the same batch of microarray chips with similar characteristics of noise. Algorithms developed under this assumption may not be applicable for analyzing data collected from heterogeneous settings, where the set of genes being monitored may be different and expression levels may be not directly comparable even for the same gene. In this paper, we propose a model, F-cluster, for mining subspace coherent patterns from heterogeneous gene expression data, which is shown effective for revealing truthful patterns and reducing spurious ones. We also develop an efficient and scalable hybrid approach that combines gene-pair based and sample-pair based pruning to generate F-clusters from multiple gene expression matrices simultaneously. The experimental results demonstrate that our model can discover significant clusters that may not be identified by previous models.
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U2 - 10.1109/SSDBM.2007.30
DO - 10.1109/SSDBM.2007.30
M3 - Conference contribution
AN - SCOPUS:46649103266
SN - 0769528686
SN - 9780769528687
T3 - Proceedings of the International Conference on Scientific and Statistical Database Management, SSDBM
BT - 19th International Conference on Scientific and Statistical Database Management, SSDBM 2007
T2 - 19th International Conference on Scientific and Statistical Database Management, SSDBM 2007
Y2 - 9 July 2007 through 11 July 2007
ER -