Chai Lian En, Loh Swee Kuan, Low Swee Thing, Mohamad Mohd Saberi, Deris Safaai, Zakaria Zalmiyah
Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, 81310 Johor, Malaysia.
Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, 81310 Johor, Malaysia.
Comput Biol Med. 2014 May;48:55-65. doi: 10.1016/j.compbiomed.2014.02.011. Epub 2014 Feb 24.
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.
许多生物学研究领域，如药物设计，都需要基因调控网络来清晰洞察和理解活细胞中的细胞过程。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起着重要作用。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。由于其重要性，已经提出了几种计算方法来从基因表达数据推断基因调控网络。在这篇综述中，讨论了六种推断方法：布尔网络、概率布尔网络、常微分方程、神经网络、贝叶斯网络和动态贝叶斯网络。从这些方法的介绍、方法学以及它们在基因调控网络构建中的最新应用等方面对这些方法进行了讨论。在讨论部分还对这些方法进行了比较。此外，还描述了这些计算方法的优缺点。
