计算机科学与探索

• 学术研究 •    

非齐次基因调控网络模型研究综述

张倩倩,胡春玲,张家瑶,李大伟,邵鸣义   

  1. 1.合肥学院 人工智能与大数据学院,合肥 230031
    2.合肥学院 安徽省城市基础设施大数据技术应用工程实验室,合肥 230031

Survey of Research on Non-homogeneous gene regulatory network models

ZHANG Qianqian, HU Chunling, ZHANG Jiayao, LI Dawei, SHAO Mingyi   

  1. 1.College of Artificial Intelligence and Big Data, Hefei University, Hefei 230031, China
    2.Anhui Province Urban Infrastructure Big Data Technology Application Engineering Laboratory, Hefei University, Hefei 230031, China

摘要: 在生物信息学领域中,基因调控网络的构建至关重要。近年来,非齐次动态贝叶斯网络已成为从基因表达时间序列数据中学习基因调控网络的一种常用建模工具。针对这一研究领域,本文梳理了基于齐次动态贝叶斯网络的基因调控网络建模方法,并从时间片划分方式和基因调控网络参数学习,综述了近十几年提出的非齐次动态贝叶斯模型,主要内容包括:(1)时间片划分方式:自由分配、连续变点过程、离散变点过程以及基于隐马尔可夫的变点过程;(2)基因调控网络参数学习,主要包括顺序耦合参数和全局耦合参数。之后对非齐次动态贝叶斯网络模型的性能进行了分析,着重介绍了这些模型对于基因调控网络建模的准确性和可靠性,以及模型之间的区别与联系,最后指出了基因调控网络构建的困难和挑战以及非齐次动态贝叶斯网络模型未来的一些研究方向。

关键词: 动态贝叶斯网络, 非齐次动态贝叶斯网络, 基因调控网络, 生物信息学

Abstract: In the field of bioinformatics, the construction of gene regulatory networks is crucial. In recent years, non-homogeneous dynamic Bayesian networks have become a common modeling tool for learning gene regulatory networks from gene expression time-series data. Aiming at this research field, this paper combs the gene regulatory network modeling method based on homogeneous dynamic Bayesian network, and summarizes the non-homogeneous dynamic Bayesian model proposed in the past ten years from the time slice division method and gene regulation network parameter learning. The main contents include:(1) Time slice division method: free allocation, continuous changepoint process, discrete changepoint process and hidden Markov-based change point process; (2) Gene regulation network parameter learning, mainly including sequence Coupling parameters and global coupling parameters. After that, the performance of the non-homogeneous dynamic Bayesian network model was analyzed, and the accuracy and reliability of these models for gene regulatory network modeling, as well as the differences and connections between the models were introduced. Finally, the difficulties and challenges of gene regulatory network construction and some future research directions of the inhomogeneous dynamic Bayesian network model are pointed out.

Key words: Dynamic Bayesian networks, Non-homogeneous dynamic Bayesian networks, Gene regulatory network, Bioinformatics