Gene networks inference using dynamic Bayesian networks
Gene networks inference using dynamic Bayesian networks
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DOI：
10.1093/bioinformatics/btg1071
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发表时间：
2003-09-01
期刊：
影响因子：
5.8
通讯作者：
d'Alche-Buc, Florence
中科院分区：
文献类型：
--
作者：
Perrin, Bruno-Edouard;Ralaivola, Liva;d'Alche-Buc, Florence
本文使用统计机器学习方法从实验数据中识别基因调控网络。提出了一种能够处理缺失变量的基因相互作用随机模型。它可以被描述为一个动态贝叶斯网络，特别适合于解决基因调控和基因表达测量的随机性。模型参数是通过EM算法的扩展版本实现的惩罚似然最大化来学习的。我们的方法是测试相对于大肠杆菌的S.O.S. DNA修复网络的实验数据。它似乎能够提取出这个网络中涉及的基因之间的主要规则。发现了一个附加的缺失变量来模拟网络的主要蛋白质。对非学习数据有较好的预测能力。这些最初的结果非常有希望：它们展示了学习算法的力量和模型捕捉基因相互作用的能力。
This article deals with the identification of gene regulatory networks from experimental data using a statistical machine learning approach. A stochastic model of gene interactions capable of handling missing variables is proposed. It can be described as a dynamic Bayesian network particularly well suited to tackle the stochastic nature of gene regulation and gene expression measurement. Parameters of the model are learned through a penalized likelihood maximization implemented through an extended version of EM algorithm.Our approach is tested against experimental data relative to the S.O.S. DNA Repair network of the Escherichia coli bacterium. It appears to be able to extract the main regulations between the genes involved in this network. An added missing variable is found to model the main protein of the network. Good prediction abilities on unlearned data are observed. These first results are very promising: they show the power of the learning algorithm and the ability of the model to capture gene interactions.