Mining Biologically Significant Co-regulation Patterns from Microarray Data

In this paper, we propose a novel model, namely g-Cluster, to mine biologically significant co-regulated gene clusters. The proposed model can (1) discover extra co-expressed genes that cannot be found by current pattern/tendency-based methods, and (2) discover inverted relationship overlooked by pattern/tendency-based methods. We also design two tree-based algorithms to mine all qualified g-Clusters. The experimental results show: (1) our approaches are effective and efficient, and (2) our approaches can find an amount of co-regulated gene clusters missed by previous models, which are potentially of high biological significance

[1]  Richard M. Karp,et al.  Discovering local structure in gene expression data: the order-preserving submatrix problem. , 2003 .

[2]  Philip S. Yu,et al.  Enhanced biclustering on expression data , 2003, Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings..

[3]  Jian Pei,et al.  Mining coherent gene clusters from gene-sample-time microarray data , 2004, KDD.

[4]  Jinze Liu,et al.  Biclustering in gene expression data by tendency , 2004 .

[5]  Wei Wang,et al.  OP-cluster: clustering by tendency in high dimensional space , 2003, Third IEEE International Conference on Data Mining.

[6]  Ya Zhang,et al.  A time-series biclustering algorithm for revealing co-regulated genes , 2005, International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume II.

[7]  M. Gerstein,et al.  Genomic analysis of gene expression relationships in transcriptional regulatory networks. , 2003, Trends in genetics : TIG.

[8]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.

[9]  Ozgur Ozturk,et al.  A time series analysis of microarray data , 2004, Proceedings. Fourth IEEE Symposium on Bioinformatics and Bioengineering.