Title :
Temporal association rules for gene regulatory networks
Author :
Baralis, Elena ; Bruno, Giulia ; Ficarra, Elisa
Author_Institution :
Dipt. di Autom. e Inf., Politec. di Torino, Turin
Abstract :
DNA hybridization arrays simultaneously measure the expression level for thousands of genes. A great challenge in the bioinformatics field is to discover gene interactions from such measurements and estimate gene networks. In this paper, we exploit data mining techniques for discovering interactions among genes based on multiple expression measurements. We present an application of the Apriori algorithm to extract temporal association rules from gene expression data. Furthermore, we address the problem of real value discretization by using both fixed thresholds and clustering techniques. Finally, we estimate the value of each rule by means of an appropriate quality index. Preliminary experimental results on Saccharomyces cerevisiae cell cycle gene expression data show the effectiveness of the proposed method.
Keywords :
DNA; bioinformatics; data mining; Apriori algorithm; DNA hybridization arrays; Saccharomyces cerevisiae cell cycle gene expression data; bioinformatics field; data mining techniques; gene regulatory networks; multiple expression measurements; quality index; temporal association rules; Association rules; Bioinformatics; Data mining; Delay effects; Gene expression; Intelligent networks; Intelligent systems; Proteins; Regulators; Signal processing; Association rules; data mining; gene networks; microarray;
Conference_Titel :
Intelligent Systems, 2008. IS '08. 4th International IEEE Conference
Conference_Location :
Varna
Print_ISBN :
978-1-4244-1739-1
Electronic_ISBN :
978-1-4244-1740-7
DOI :
10.1109/IS.2008.4670511