Top-down GGM (12MB) - An R package for inferring a multilayered gene regulatory network (ML-hGRN) mediated by a TF. The input data is the high-throughput data from a transient expression process upon a TF is perturbed or the overexpression lines of a TF.
Bottom-up GGM (18MB) - A software for building a multilayered hierarchical gene regulatory network regulating a biological pathway or process.
BWERF (3.5MB) - Backward Elimination Random Forest. A software for building a multilayered hierarchical gene regulatory network regulating a biological pathway or process.
CollaborativeNet (20MB) - A method for recognizing the regulatory genes, such as TFs, that collaboratively regulate a complex trait, a pathway or a biological process.
HB-PLS (2.1MB) - Huber-Berhu Partial Least Squares. An R package for identifying pathway or biological process regulators.
JRmGRN(61 KB) - Joint reconstruction of multiple gene regulatory networks (JRmGRNs). An R package for constructing mGRNs and identifying common hub genes using the high-throughput transcriptomic data from multiple cell-types/tissues, or multiple conditions.
GNItools (46MB) - A gene network inference R package, which includes BWERF, JRmGRN, and HB-PLS, where JRmGRNs represents Joint Reconstruction of multiple Gene Regulatory Networkss using the data from multiple tissues or conditions.
TF-Finder (2.2MB) - An automated software R package for recognizing TFs involved in a biological process using adaptive sparse canonical correlation analysis (ASCCA) and enrichment test.
Eight Gene Association Methods (879KB) - R code for Spearman Rank Correlation, Weighted Rank Correlation, Kendall Rank Correlation, Hoeffding’s D measure, Theil-Sen, Rank Theil-Sen, Distance Covariance, and Pearson Correlation.
TGPred (1.5MB) - A Python package contains six methods for identifying the target genes of a TF, which include four methods, Huber-ENET, MSE-ENET, Huber-LASSO, and MSE-LASSO, for identifying target genes of a TF, and two methods, Huber-Net and MSE-Net for identifying pathway gene regulatory networks.
TGPred (1.4MB) - An R package contains six methods: four methods, Huber-ENET, MSE-ENET, Huber-LASSO, and MSE-LASSO, for identifying target genes of a TF, and two methods, Huber-Net and MSE-Net, for identifying pathway gene regulatory networks.
TGMI(9MB) - An R package for inferring pathway or biological process regulators using conditional mutual information.
SPLS (33MB) - Sparse Partial least Squares. An R package for identifying regulators that govern a pathway or a biological process.
The publication(s) of each software is(are) enclosed in each package.