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Inference of Gene Regulatory Networks Based on Nonlinear Ordinary Differential Equations.
Bioinformatics ( IF 5.8 ) Pub Date : 2020-10-20 , DOI: 10.1093/bioinformatics/btaa032
Baoshan Ma 1 , Mingkun Fang 1 , Xiangtian Jiao 1
Affiliation  

Gene regulatory networks (GRNs) capture the regulatory interactions between genes, resulting from the fundamental biological process of transcription and translation. In some cases, the topology of GRNs is not known, and has to be inferred from gene expression data. Most of the existing GRNs reconstruction algorithms are either applied to time-series data or steady-state data. Although time-series data include more information about the system dynamics, steady-state data imply stability of the underlying regulatory networks.

中文翻译:

基于非线性常微分方程的基因调控网络推论。

基因调控网络(GRN)捕获基因之间的调控相互作用,这是转录和翻译的基本生物学过程导致的。在某些情况下,GRN的拓扑是未知的,必须从基因表达数据中推断出来。现有的大多数GRN重建算法都应用于时间序列数据或稳态数据。尽管时间序列数据包含有关系统动态的更多信息,但稳态数据意味着基础监管网络的稳定性。
更新日期:2020-12-08
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