One of the most interesting, difficult, and potentially useful topics in computational biology is the inference of gene regulatory networks (GRNs) from expression data. Although researchers have been working on this topic for more than a decade and much progress has been made, it remains an unsolved problem and even the most sophisticated inference algorithms are far from perfect. In this paper, we review the latest developments in network inference, including state-of-the-art algorithms like PIDC, Phixer, and more. We also discuss unsolved computational challenges, including the optimal combination of algorithms, integration of multiple data sources, and pseudo-temporal ordering of static expression data. Lastly, we discuss some exciting applications of network inference in cancer research, and provide a list of useful software tools for researchers hoping to conduct their own network inference analyses.

中文翻译：

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系统生物学中的网络推理：最新发展、挑战和应用。


计算生物学中最有趣、最困难且可能有用的主题之一是从表达数据推断基因调控网络（GRN）。尽管研究人员已经在这个主题上研究了十多年，并取得了很大进展，但这仍然是一个未解决的问题，即使是最复杂的推理算法也远非完美。在本文中，我们回顾了网络推理的最新发展，包括 PIDC、Phixer 等最先进的算法。我们还讨论了未解决的计算挑战，包括算法的最佳组合、多个数据源的集成以及静态表达数据的伪时间排序。最后，我们讨论了网络推理在癌症研究中的一些令人兴奋的应用，并为希望进行自己的网络推理分析的研究人员提供了一系列有用的软件工具。