Computational Biology Division, Translational Genomics Research Institute, Phoenix, AZ 85004, USA.
Curr Genomics. 2009 Sep;10(6):416-29. doi: 10.2174/138920209789177610.
The advent of high-throughput technology like microarrays has provided the platform for studying how different cellular components work together, thus created an enormous interest in mathematically modeling biological network, particularly gene regulatory network (GRN). Of particular interest is the modeling and inference on time-series data, which capture a more thorough picture of the system than non-temporal data do. We have given an extensive review of methodologies that have been used on time-series data. In realizing that validation is an impartible part of the inference paradigm, we have also presented a discussion on the principles and challenges in performance evaluation of different methods. This survey gives a panoramic view on these topics, with anticipation that the readers will be inspired to improve and/or expand GRN inference and validation tool repository.
高通量技术（如微阵列）的出现为研究不同细胞成分如何协同工作提供了平台，因此人们对数学建模生物网络，特别是基因调控网络（GRN）产生了极大的兴趣。特别感兴趣的是对时间序列数据进行建模和推断，它比非时间数据更全面地描述了系统。我们对已经用于时间序列数据的方法进行了广泛的回顾。在认识到验证是推断范例不可或缺的一部分的同时，我们还讨论了不同方法性能评估的原则和挑战。本调查全面介绍了这些主题，希望读者受到启发，改进和/或扩展 GRN 推断和验证工具库。
