Conditional mutual inclusive information enables accurate
quantification of associations in gene regulatory networks


MATLAB source code, dataset and tutorial

This page provides the free MATLAB source codes, dataset and Tutorial.



CMI2NI is a software for inferring gene regulatory networks from gene expression data. It is a novel method using a new proposed concept of Conditional Mutual Inclusive Information (CMI2) which can accurately measure direct dependences between genes. Given the small size samples of gene expression data, CMI2NI can not only infer the correct topology of the regulation networks but also accurately quantify the dependence or regulation strength between genes. CMI2NI provides a useful tool to infer gene regulatory networks.

Source Code: CMI2NI ;
Example: Example_CMI2NI ;

Data: example_data;

Tutorial: Tutorial for CMI2NI.
[1] Xiujun Zhang, Keqin Liu, Zhi-Ping Liu, BĂ©atrice Duval, Jean-Michel Richer, Xing-Ming Zhao, Jin-Kao Hao and Luonan Chen. NARROMI: a noise and redundancy reduction technique improves accuracy of gene regulatory network inference. Bioinformatics, 29(1): 106-113, 2013.(pdf)
[2] Xiujun Zhang, Xing-Ming Zhao, Kun He, Le Lu, Yongwei Cao, Jingdong Liu, Jin-Kao Hao, Zhi-Ping Liu, Luonan Chen. Inferring gene regulatory networks from gene expression data by path consistency algorithm based on conditional mutual information. Bioinformatics, 28 (1):98- 104, 2012.(pdf)


The MATLAB codes and dataset are free to use. If you encounter any problem, please do not hesitate to contact us at Thanks!