Detecting reliable gene interactions by a hierarchy of Bayesian network classifiers
Detecting reliable gene interactions by a hierarchy of Bayesian network classifiers
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DOI：
10.1016/j.cmpb.2008.02.010
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发表时间：
2008-08-01
影响因子：
6.1
通讯作者：
Larranaga, Pedro
中科院分区：
文献类型：
--
作者：
Armananzas, Ruben;Inza, Inaki;Larranaga, Pedro
基因相互作用网络的主要目的是绘制基因组研究中看不到的基因之间的关系。DNA微阵列允许同时测量数千个基因的基因表达。这些数据构成了基因网络诱导的数字种子。本文提出了一种利用贝叶斯分类器、变量选择和自举重采样来构建基因网络的新方法。贝叶斯分类器诱导的相互作用既基于表达水平，也基于监督变量的表型信息。特征选择和自举重采样为整个过程增加了可靠性和鲁棒性，从而消除了假阳性结果。所有诱导模型之间的共识产生了依赖关系的层次结构，因此，变量的层次结构。生物学家可以定义模型层次结构的深度，因此涉及的相互作用和基因3的集合可以从稀疏到密集的集合变化。实验结果表明，这些网络在分类任务上表现良好。生物学验证与先前的生物学发现相吻合，为未来的研究开辟了新的假设。2008爱思唯尔爱尔兰有限公司版权所有。
The main purpose of a gene interaction network is to map the relationships of the genes that are out of sight when a genomic study is tackled. DNA microarrays allow the measure of gene expression of thousands of genes at the same time. These data constitute the numeric seed for the induction of the gene networks. in this paper, we propose a new approach to build gene networks by means of Bayesian classifiers, variable selection and bootstrap resampling. The interactions induced by the Bayesian classifiers are based both on the expression levels and on the phenotype information of the supervised variable. Feature selection and bootstrap resampling add reliability and robustness to the overall process removing the false positive findings. The consensus among all the induced models produces a hierarchy of dependences and, thus, of variables. Biologists can define the depth level of the model hierarchy so the set of interactions and gene 3 involved can vary from a sparse to a dense set. Experimental results show how these networks perform well on classification tasks. The biological validation matches previous biological findings and opens new hypothesis for future studies. (C) 2008 Elsevier Ireland Ltd. All rights reserved.