CABERNET is a Cytoscape 3.2.0 app for the generation, the simulation, the analysis and the visualization of Boolean models of gene regulatory networks, particularly focused on the investigation of their robustness.
Netview is a free web tool that allow end users to explore the human and mouse gene regulatory networks. Users can query the system by providing a gene identifier and the boundaries of the subnetwork being explored. The system provides the list of interactions, the enriched Gene Ontology Terms together with a graphical representation of the subnetwork.
GENIE3 is an algorithm for the inference of gene regulatory networks from expression data. It decomposes the prediction of a regulatory network between p genes into p different regression problems. In each of the regression problems, the expression pattern of one of the genes (target gene) is predicted from the expression patterns of all the other genes (input genes), using tree-based ensemble methods Random Forests or Extra-Trees. The importance of an input gene in the prediction of the target gene expression pattern is taken as an indication of a putative regulatory link.
dynGENIE3 : Extension of GENIE3 for time series data
NARROMI is a MATLAB program for inferring gene regulatory networks from gene expression data. It is a novel method combining ordinary differential equation based recursive optimization (RO) and information-theory based mutual information (MI).
CMI2NI (CMI2-based network inference) 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.
BNArray is a systemized tool developed in R. It facilitates the construction of gene regulatory networks from DNA microarray data by using Bayesian network. Significant submodules of regulatory networks with high confidence are reconstructed using our extended sub-network mining algorithm for directed graphs.
A novel network model PathRNet and a reconstruction approach PATTERN are proposed for reconstructing the context specific time varying regulatory networks by integrating microarray gene expression profiles and existing knowledge of pathways and transcription factors.
mirConnX is a user-friendly web interface for inferring, displaying and parsing mRNA and microRNA (miRNA) gene regulatory networks. mirConnX combines sequence information, and computational predictions with gene expression data analysis to create a disease-specific, genome-wide regulatory network.