Omranian Nooshin, Nikoloski Zoran
Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, Potsdam-Golm, 14476, Germany.
Methods Mol Biol. 2017;1629:283-295. doi: 10.1007/978-1-4939-7125-1_18.
The goal of the gene regulatory network (GRN) inference is to determine the interactions between genes given heterogeneous data capturing spatiotemporal gene expression. Since transcription underlines all cellular processes, the inference of GRN is the first step in deciphering the determinants of the dynamics of biological systems. Here, we first describe the generic steps of the inference approaches that rely on similarity measures and group the similarity measures based on the computational methodology used. For each group of similarity measures, we not only review the existing approaches but also describe specifically the detailed steps of the existing state-of-the-art algorithms.
基因调控网络（GRN）推理的目标是在给定捕获时空基因表达的异构数据的情况下，确定基因之间的相互作用。由于转录是所有细胞过程的基础，GRN的推理是破译生物系统动态决定因素的第一步。在这里，我们首先描述基于相似性度量的推理方法的一般步骤，并根据所使用的计算方法对相似性度量进行分组。对于每组相似性度量，我们不仅回顾现有方法，还具体描述现有最先进算法的详细步骤。
