Abstract
Construction gene regulation networks can provide insights into the understanding the molecular mechanisms underlying important biological processes. We present a novel association rule mining for building large-scale gene regulation networks from microarray data. Gene expression microarray data typically contains a very high gene dimension and a very low sample size, rendering a great challenge for existing association rule mining algorithms. In this paper, we develop a novel algorithm, HCMiner, to mine high-correlation association rules from microarray data. HCMiner initially overlapping partitions the dimension of genes according to their correlations and introduces the support-free framework for mining association rules. Several experiments on Yeast dataset show that the proposed algorithm outperforms existing algorithms with respect to scalability and effectiveness.
This research is partly supported by the National Natural Science Foundation of China (No. 60703105) and the Natural Science Foundation of Shaanxi Province (No. 2007F27). All opinions, findings, conclusions and recommendations in this paper are those of the authors and do not necessarily reflect the views of the funding agencies.
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Shang, X., Zhao, Q., Li, Z. (2009). Mining High-Correlation Association Rules for Inferring Gene Regulation Networks. In: Pedersen, T.B., Mohania, M.K., Tjoa, A.M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2009. Lecture Notes in Computer Science, vol 5691. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03730-6_20
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DOI: https://doi.org/10.1007/978-3-642-03730-6_20
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