基因调控网络的父节点筛选贝叶斯建模方法

doi:10.12068/j.issn.1005-3026.2020.02.002

东北大学学报:自然科学版 ›› 2020, Vol. 41 ›› Issue (2): 158-162.DOI: 10.12068/j.issn.1005-3026.2020.02.002

基因调控网络的父节点筛选贝叶斯建模方法

曲璐渲¹，郭上慧¹，王之琼^1,2，信俊昌^3,4

(1. 东北大学医学与生物信息工程学院，辽宁沈阳110169; 2. 沈阳东软智能医疗科技研究院有限公司，辽宁沈阳110179;3. 东北大学计算机科学与工程学院，辽宁沈阳110169; 4. 辽宁省大数据管理与分析重点实验室，辽宁沈阳110169)

收稿日期:2019-04-17 修回日期:2019-04-17 出版日期:2020-02-15 发布日期:2020-03-06
通讯作者: 曲璐渲
作者简介:曲璐渲(1987-)，女，黑龙江哈尔滨人，东北大学博士研究生; 信俊昌(1977-)，男，辽宁辽阳人，东北大学教授，博士生导师. 冯明杰(1971-)，男，河南禹州人，东北大学副教授; 王恩刚(1962-)，男，辽宁沈阳人，东北大学教授，博士生导师.
基金资助:
国家自然科学基金资助项目(61472069，61402089)；中央高校基本科研业务费专项资金资助项目(N180101028，N180408019，N161602003，N160601001)；中国博士后科学基金资助项目(2018M641705，2019T120216)；沈阳东软智能医疗科技研究院有限公司开放课题基金资助项目(NRIHTOP1802).

Modelling of Gene Regulatory Networks by Parent Node Screening-Based Bayesian Method

QU Lu-xuan¹， GUO Shang-hui¹， WANG Zhi-qiong^1,2， XIN Jun-chang^3,4

1. School of Medicine & Biological Information Engineering， Northeastern University， Shenyang 110169， China; 2. Neusoft Research of Intelligent Healthcare Technology Co.， Ltd.， Shenyang 110179， China; 3. School of Computer Science & Engineering， Northeastern University， Shenyang 110169， China; 4. Key Laboratory of Big Data Management and Analytics

Received:2019-04-17 Revised:2019-04-17 Online:2020-02-15 Published:2020-03-06
Contact: WANG Zhi-qiong
About author:-
Supported by:
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摘要/Abstract

摘要： 在构建基因调控网络的方法中，贝叶斯网络模型可以直观地表达基因间的调控关系，但在结构学习时的复杂度极高，使得网络建模效率较低且规模有限.因此，本文提出一种基于父节点筛选的贝叶斯网络(parent node screening based Bayesian network， PS-BN)建模方法.PS-BN方法将关联模型与贝叶斯网络模型相结合，在充分利用贝叶斯网络模型结构学习搜索策略的前提下，先基于父节点筛选方法去除部分冗余信息，以达到缩减搜索空间的目的.实验结果表明，与传统的贝叶斯网络模型方法相比，PS-BN方法极大提升了基因调控网络构建效率，同时准确率有所提高.

关键词: 基因调控网络, 父节点筛选, 贝叶斯网络模型, 关联模型, 结构学习

Abstract: Among the methods for modeling gene regulation networks， Bayesian network model can intuitively express the regulatory relationship between genes. However， due to the high complexity of Bayesian network model in the structure learning， the efficiency of the gene regulation networks modeling is low and the scale of the reconstructed network is limited. Therefore， this paper proposed a method which is called the parent node screening-based Bayesian network(PS-BN). The PS-BN method combines the correlation model with Bayesian network model. Under the premise of making full use of the search strategy of structure learning in Bayesian network model， the parent node screening method is used to remove some redundant nodes， thus reducing the search space. The experimental results show that compared with the Bayesian network model， the PS-BN method greatly improves the efficiency of modeling gene regulatory networks while improving the accuracy.

Key words: gene regulatory networks, parent node screening, Bayesian network model, correlation model, structure learning

中图分类号:

TP181

曲璐渲，郭上慧，王之琼，信俊昌. 基因调控网络的父节点筛选贝叶斯建模方法[J]. 东北大学学报:自然科学版, 2020, 41(2): 158-162.

QU Lu-xuan， GUO Shang-hui， WANG Zhi-qiong， XIN Jun-chang. Modelling of Gene Regulatory Networks by Parent Node Screening-Based Bayesian Method[J]. Journal of Northeastern University Natural Science, 2020, 41(2): 158-162.

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基因调控网络的父节点筛选贝叶斯建模方法

Modelling of Gene Regulatory Networks by Parent Node Screening-Based Bayesian Method

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