Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/39803
Title: A novel data mining algorithm for reconstructing gene regulatory networks from microarray data
Authors: Ma, PCH
Chan, KCC 
Keywords: Bioinformatics
Data mining
Gene regulatory networks (GRNs)
Issue Date: 2006
Source: Proceedings of the 21st Annual ACM Symposium on Applied Computing (ACMSAC06-Bioinformatics), Dijon, France, 23-27 April 2006, p. 202-203 How to cite?
Abstract: In this paper, we propose a novel data mining algorithm for reconstructing gene regulatory networks (GRNs) from microarray data. By making use of the proposed probabilistic measure, it is able to mine noisy, high dimensional expression data for interesting association patterns of genes without the need for additional feature selection procedures. Moreover, it can make explicit hidden patterns discovered for possible biological interpretation and also predict gene expression patterns in the unseen tissue samples. Experimental results on real expression data show that it is very effective and the discovered association patterns reveal biologically meaningful regulatory relationships of genes that could help users reconstructing the underlying structures of GRNs.
URI: http://hdl.handle.net/10397/39803
ISBN: 1-59593-108-2
DOI: 10.1145/1141277.1141323
Appears in Collections:Conference Paper

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