Review on statistical methods for gene network reconstruction using expression data
Review on statistical methods for gene network reconstruction using expression data
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DOI：
10.1016/j.jtbi.2014.03.040
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发表时间：
2014-12-07
影响因子：
2
通讯作者：
Huang, Haiyan
中科院分区：
文献类型：
--
作者：
Wang, Y. X. Rachel;Huang, Haiyan
网络建模已被证明是分析细胞内部运作的基本工具。它彻底改变了我们对生物过程的理解，并为疾病生物标志物的发现做出了重大贡献。人们付出了大量的努力来利用高通量技术生成的功能基因组数据集重建各种类型的生化网络。本文讨论了使用基因表达数据重建基因调控网络的统计方法。我们特别强调了在估计基因相互作用、推断因果关系和对调节行为的时间变化进行建模所涉及的问题中所取得的进展和尚未解决的挑战。随着技术的快速进步已经提供了多样化的大规模基因组数据，我们还调查了整合所有这些额外数据的方法，以实现更好、更准确的基因网络推理。 (C) 2014 Elsevier Ltd. 保留所有权利。
Network modeling has proven to be a fundamental tool in analyzing the inner workings of a cell. It has revolutionized our understanding of biological processes and made significant contributions to the discovery of disease biomarkers. Much effort has been devoted to reconstruct various types of biochemical networks using functional genomic datasets generated by high-throughput technologies. This paper discusses statistical methods used to reconstruct gene regulatory networks using gene expression data. In particular, we highlight progress made and challenges yet to be met in the problems involved in estimating gene interactions, inferring causality and modeling temporal changes of regulation behaviors. As rapid advances in technologies have made available diverse, large-scale genomic data, we also survey methods of incorporating all these additional data to achieve better, more accurate inference of gene networks. (C) 2014 Elsevier Ltd. All rights reserved.