Abstract
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.
Supported by National Natural Science Foundation of China under grant No.60573089, 60273079 and 60473074.
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Yang, J., Wang, H., Wang, W., Yu, P.S.: Enhanced biclustering on expression data. In: BIBE, pp. 321–327 (2003)
Ben-Dor, A., Chor, B., Karp, R.M., Yakhini, Z.: Discovering local structure in gene expression data: The order-preserving submatrix problem. Journal of Computational Biology 10, 373–384 (2003)
Yu, H., Luscombe, N., Qian, J., Gerstein, M.: Genomic analysis of gene expression relationships in transcriptional regulatory networks. Trends Genet 19, 422–427 (2003)
Zhang, Y., Zha, H., Chu, C.H.: A time-series biclustering algorithm for revealing co-regulated genes. In: Proc. of ITCC 2005, pp. 32–37 (2005)
Breitkreutz, B.J., Stark, C., Tyers, M.: (yeast grid)
Liu, J., Wang, W.: Op-cluster: Clustering by tendency in high dimensional space. In: Proc. of ICDM 2003 Conference, pp. 187–194 (2003)
Liu, J., Yang, J., Wang, W.: Biclustering in gene expression data by tendency. In: Proc. of CSB 2004 Conference, pp. 182–193 (2004)
Erdal, S., Ozturk, O., Armbruster, D.L., et al.: A time series analysis of microarray data. In: Proc. of BIBE conference, pp. 366–378 (2004)
Golub, T.R., Slonim, D.K., Tamayo, P., et al.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999)
Jiang, D., Pei, J., Ramanathany, M., Tang, C., Zhang, A.: Mining coherent gene clusters from gene-sample-time microarray data. In: 10th ACM SIGKDD Conference, pp. 430–439 (2004)
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© 2006 Springer-Verlag Berlin Heidelberg
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Zhao, Y., Yin, Y., Wang, G. (2006). Mining Biologically Significant Co-regulation Patterns from Microarray Data. In: Wang, GY., Peters, J.F., Skowron, A., Yao, Y. (eds) Rough Sets and Knowledge Technology. RSKT 2006. Lecture Notes in Computer Science(), vol 4062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11795131_59
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DOI: https://doi.org/10.1007/11795131_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36297-5
Online ISBN: 978-3-540-36299-9
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