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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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
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