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.
Original language | English |
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Title of host publication | Applied Computing 2006 - The 21st Annual ACM Symposium on Applied Computing - Proceedings of the 2006 ACM Symposium on Applied Computing |
Pages | 202-203 |
Number of pages | 2 |
Volume | 1 |
Publication status | Published - 21 Nov 2006 |
Event | 2006 ACM Symposium on Applied Computing - Dijon, France Duration: 23 Apr 2006 → 27 Apr 2006 |
Conference
Conference | 2006 ACM Symposium on Applied Computing |
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Country | France |
City | Dijon |
Period | 23/04/06 → 27/04/06 |
Keywords
- Bioinformatics
- Data mining
- Gene regulatory networks (GRNs)
ASJC Scopus subject areas
- Software