| 基于生物先验数据融合构建非平稳基因调控网络 |
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| 作者姓名： | 倪小虹 孙应飞 |
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| 作者单位： | 中国科学院大学电子电气与通信工程学院, 北京 100049 |
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| 基金项目： | Supported by the National Nature Science Foundation of China(60702035), Nature Science Foundation of Zhejiang Province(Y6090164) |
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| 摘 要： | 为了构建基因调控网络,提出一个基于生物先验数据融合构建非平稳动态贝叶斯网络结构的方法.该方法基于高斯混合网络模型,改变点过程和独立的能量函数.利用可逆跳跃马尔科夫蒙特卡罗抽样算法,把整个非平稳过程分解成若干平稳子片断,推断网络结构以及先验数据对网络的影响.在仿真和生物数据上测试该方法,结果显示该方法提高了网络重构的精度.
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| 关 键 词： | 贝叶斯网络 基因调控网络 马尔科夫蒙特卡罗抽样 改变点过程 |
| 收稿时间： | 2012-09-12 |
| 修稿时间： | 2013-03-26 |
Modelling non-stationary gene regulatory networks by combining microarrys with biological knowledge |
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| Authors: | NI Xiao-Hong SUN Ying-Fei |
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| Institution: | School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China |
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| Abstract: | In order to construct gene regulatory network, we propose a non-stationary dynamic Bayesian networks method that systematically integrates expression data with multiple sources of prior knowledge. Our method is based on Gaussian mixture Bayesian network model, change point process, and separate energy function of prior knowledge. Using an reversible jump Markov chain Monte Carlo sampling algorithm, we divide data into disjunct compartments, infer network structures, and measure the influence of the respective prior knowledge. Finally, we apply our approach to treat both synthetic data and biological data. The results show that the proposed method improves the network reconstruction accuracy. |
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| Keywords: | Bayesian networks gene regulatory networks Markov chain Monte Carlo change point process |
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