A review on the computational approaches for gene regulatory network construction
A review on the computational approaches for gene regulatory network construction
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DOI：
10.1016/j.compbiomed.2014.02.011
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发表时间：
2014-05-01
影响因子：
7.7
通讯作者：
Zakaria, Zalmiyah
中科院分区：
文献类型：
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作者：
Chai, Lian En;Loh, Swee Kuan;Zakaria, Zalmiyah
许多生物学研究领域，如药物设计，都需要基因调控网络来提供对活细胞中细胞过程的清晰洞察和理解。这是因为基因及其产物之间的相互作用在许多分子过程中发挥着重要作用。基因调控网络可以作为研究人员观察基因之间关系的蓝图。由于它的重要性，人们已经提出了几种计算方法来从基因表达数据中推断基因调控网络。本文讨论了六种推理方法：布尔网络、概率布尔网络、常微分方程式、神经网络、贝叶斯网络和动态贝叶斯网络。本文从这些方法的介绍、方法学及其在基因调控网络构建中的应用等方面对这些方法进行了讨论。这些方法也将在讨论部分进行比较。此外，还描述了这些计算方法的优点和缺点。(C)2014爱思唯尔有限公司。保留所有权利。
Many biological research areas such as drug design require gene regulatory networks to provide clear insight and understanding of the cellular process in living cells. This is because interactions among the genes and their products play an important role in many molecular processes. A gene regulatory network can act as a blueprint for the researchers to observe the relationships among genes. Due to its importance, several computational approaches have been proposed to infer gene regulatory networks from gene expression data. In this review, six inference approaches are discussed: Boolean network, probabilistic Boolean network, ordinary differential equation, neural network, Bayesian network, and dynamic Bayesian network. These approaches are discussed in terms of introduction, methodology and recent applications of these approaches in gene regulatory network construction. These approaches are also compared in the discussion section. Furthermore, the strengths and weaknesses of these computational approaches are described. (C) 2014 Elsevier Ltd. All rights reserved.