Temporal association rules for gene regulatory networks

Elena Baralis, G. Bruno, E. Ficarra
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引用次数: 6

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.
基因调控网络的时间关联规则
DNA杂交阵列同时测量数千个基因的表达水平。从这些测量中发现基因相互作用并估计基因网络是生物信息学领域的一大挑战。在本文中,我们利用数据挖掘技术来发现基于多个表达测量的基因之间的相互作用。我们提出了一种应用Apriori算法从基因表达数据中提取时间关联规则的方法。此外,我们通过使用固定阈值和聚类技术来解决实值离散化问题。最后,我们通过适当的质量指标来估计每条规则的值。对酿酒酵母细胞周期基因表达数据的初步实验结果表明了该方法的有效性。
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