标题： 系统生物学中的网络推断：最新进展、挑战与应用 显示英文标题

标题： Network Inference in Systems Biology: Recent Developments, Challenges, and Applications

摘要： 在计算生物学中，从表达数据推断基因调控网络（GRNs）是一个最有趣、最难且潜在很有用的主题。 尽管研究人员已经在这个主题上工作了十多年，并取得了很大进展，但这个问题仍未解决，甚至最复杂的推断算法也远非完美。 在本文中，我们回顾了网络推断的最新发展，包括像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、多种数据源的整合以及静态表达数据的伪时间排序。 最后，我们讨论了网络推断在癌症研究中的一些令人兴奋的应用，并为希望进行自身网络推断分析的研究人员提供了一份有用的软件工具列表。 显示英文摘要

摘要： 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.