Pan Chu, Chen Yanlin
College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, China.
School of Software, Henan University of Engineering, Zhengzhou, Henan, China.
BMC Bioinformatics. 2024 Dec 18;25(1):382. doi: 10.1186/s12859-024-05996-z.
Using information measures to infer biological regulatory networks can capture nonlinear relationships between variables. However, it is computationally challenging, and there is a lack of convenient tools.
We introduce Informeasure, an R package designed to quantify nonlinear dependencies in biological regulatory networks from an information theory perspective. This package compiles a comprehensive set of information measurements, including mutual information, conditional mutual information, interaction information, partial information decomposition, and part mutual information. Mutual information is used for bivariate network inference, while the other four estimators are dedicated to trivariate network analysis.
Informeasure is a turnkey solution, allowing users to utilize these information measures immediately upon installation. Informeasure is available as an R/Bioconductor package at https://bioconductor.org/packages/Informeasure .
使用信息度量来推断生物调控网络可以捕捉变量之间的非线性关系。然而，这在计算上具有挑战性，并且缺乏便捷的工具。
我们引入了Informeasure，这是一个R包，旨在从信息论的角度量化生物调控网络中的非线性依赖性。该包汇编了一套全面的信息度量，包括互信息、条件互信息、交互信息、部分信息分解和部分互信息。互信息用于双变量网络推断，而其他四个估计器则专门用于三变量网络分析。
Informeasure是一个交钥匙解决方案，允许用户在安装后立即使用这些信息度量。Informeasure可作为R/Bioconductor包在https://bioconductor.org/packages/Informeasure获取。
