论文标题
从时间序列转录组学到基因调节网络：关于推理方法的综述
From time-series transcriptomics to gene regulatory networks: a review on inference methods
论文作者
论文摘要
基因调节网络的推断已有20年了，一直是一个积极的研究领域，导致基于各种假设和方法的复杂推理算法的发展。随着对更准确和更强大的模型的需求始终不断增长，推论问题仍然具有广泛的科学利益。通过基因调节网络对生物系统的抽象表示代表了研究此类系统，编码不同数量和类型信息的强大方法。在这篇综述中，我们总结了基于时间序列的不同类型的推理算法，概述了基因调节网络在计算生物学中的主要应用。这篇综述旨在将监管网络推理工具的最新概述介绍给新的主题的生物学家和研究人员，并指导他们选择最适合其问题，目的和实验数据的适当推理方法。
Inference of gene regulatory networks has been an active area of research for around 20 years, leading to the development of sophisticated inference algorithms based on a variety of assumptions and approaches. With the always increasing demand for more accurate and powerful models, the inference problem remains of broad scientific interest. The abstract representation of biological systems through gene regulatory networks represents a powerful method to study such systems, encoding different amounts and types of information. In this review, we summarize the different types of inference algorithms specifically based on time-series transcriptomics, giving an overview of the main applications of gene regulatory networks in computational biology. This review is intended to give an updated overview of regulatory networks inference tools to biologists and researchers new to the topic and guide them in selecting the appropriate inference method that best fits their questions, aims and experimental data.
