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.
PCA-CMI is a MATLAB program for inferring gene regulatory networks from gene expression data. It is a novel method based on path consistency algorithm and conditional mutual information, which consider the non-linear dependence and topological structure of GRNs. In this algorithm, the (conditional) dependence between a pair of genes is represented by the CMI between them. With the general hypothesis of Gaussian distribution underlying gene expression data, CMI between a pair of genes is computed by a concise formula involving the covariance matrices of the related gene expression profiles.
NARROMI is software for improving accuracy of GRNs inference. It is a novel method combining ordinary differential equation based recursive optimization (RO) and information-theory based mutual information (MI). In this algorithm, both noisy regulations with low pair-wise correlations and redundant regulations from indirect regulators are removed by measuring MI and implementing RO, respectively. In particular, it is the first one to handle the redundancy problem in GRNs for model based network inference methods.