[1]董自健,宋铁成,袁创.基于基因扰动及变分逼近技术的基因调控网络推断[J].东南大学学报(自然科学版),2013,43(6):1147-1151.[doi:10.3969/j.issn.1001-0505.2013.06.003]
 Dong Zijian,Song Tiecheng,Yuan Chuang.Variational approximation inference for gene regulatory networks from gene perturbations[J].Journal of Southeast University (Natural Science Edition),2013,43(6):1147-1151.[doi:10.3969/j.issn.1001-0505.2013.06.003]
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基于基因扰动及变分逼近技术的基因调控网络推断()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
43
期数:
2013年第6期
页码:
1147-1151
栏目:
生物医学工程
出版日期:
2013-11-20

文章信息/Info

Title:
Variational approximation inference for gene regulatory networks from gene perturbations
作者:
董自健12宋铁成1袁创3
1东南大学信息科学与工程学院, 南京 210096; 2淮海工学院电子工程学院, 连云港 222005; 3香港理工大学医疗科技及信息学系, 香港
Author(s):
Dong Zijian12 Song Tiecheng1 Yuan Chuang3
1School of Information Science and Engineering, Southeast University, Nanjing 210096, China
2School of Electronic Engineering, Huaihai Institute of Technology, Lianyungang 222005, China
3Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, China
关键词:
基因调控网络 验证性因子分析模型 变分逼近 重要性抽样
Keywords:
gene regulatory network(GRN) confirmatory factor analysis(CFA)model variational approximation importance sampling
分类号:
Q78
DOI:
10.3969/j.issn.1001-0505.2013.06.003
摘要:
为了有效提高基因调控网络推断的精度,基于基因表达数据和基因扰动数据,将基因调控网络建模为结构方程模型,并进一步转化为验证性因子分析(CFA)模型,然后使用贝叶斯方法求解CFA模型参数.在贝叶斯分析中,为减少计算量,不采用常用的马尔科夫-蒙特卡洛抽样方法,而是采用变分逼近技术对参数的联合后验分布进行因式化,并获得参数的后验包含概率分布及参数的后验分布.同时使用重要性抽样技术对CFA模型的推断参数进行加权平均.仿真结果表明,CFA模型和变分逼近技术是有效和可靠的.根据实验数据,使用所提算法推导了具有35个基因的酵母基因调控网络.
Abstract:
To improve the inference accuracy of gene regulatory networks(GRN), using both gene perturbations and gene expression data, GRN is modeled as a structural equation model(SEM), and further transformed into the confirmatory factor analysis(CFA)model. The Bayesian approach is used to infer the parameters of the regulatory networks. Instead of the Markov chain Monte Carlo(MCMC)method, the variational approximation method(VAM)is applied for its lower computation cost, which factorizes the joint posterior distribution of parameters, and obtains the posterior inclusion probability distribution and the posterior distribution of parameters. An importance sampling technique is then applied to obtain the weighted average of the CFA inferred parameters. Simulations are carried out to verify the effectiveness and reliability of the CFA model and the variational approximation. Based on the experimental data, the regulatory interactions among a 35 yeast genes network are identified with the proposed VAM algorithm.

参考文献/References:

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备注/Memo

备注/Memo:
作者简介: 董自健(1973—),男,博士,副教授;宋铁成(联系人),男,博士,教授,博士生导师,songtc@seu.edu.cn.
基金项目: 国家自然科学基金资助项目(61271207)、江苏省自然科学基金资助项目(BK2011398)、江苏省高校优秀中青年骨干教师和校长境外研修计划资助项目.
引文格式: 董自健,宋铁成,袁创.基于基因扰动及变分逼近技术的基因调控网络推断[J].东南大学学报:自然科学版,2013,43(6):1147-1151. [doi:10.3969/j.issn.1001-0505.2013.06.003]
更新日期/Last Update: 2013-11-20