Protein Graph Library

Single-cell Transcriptome and Regulome Analysis Pipeline

A R/MATLAB package to construct and compare gene regulatory networks from single-cell transcriptomic data

Deciphering driver regulators of cell fate decisions from single-cell RNA-seq data

R package: Simulate Expression data from igraph network using mvtnorm (CRAN; JOSS)

TIMEOR: Trajectory Inference and Mechanism Exploration with Omics Data in R

Simulation and inference of gene regulatory networks based on transcriptional bursting

Source code for the paper "Fast and Accurate Inference of Gene Regulatory Networks through Robust Precision Matrix Estimation", by Passemiers et al.

Gene Regulatory Interaction Network Simulator - GRiNS

A Nextflow pipeline demonstrating how to train graph neural networks for gene regulatory network reconstruction using DREAM5 data.

A tool for gene regulatory inference

Gene regulatory network reconstruction using random forest regression from only gene expression data from Single Cell RNA Seq Data. This project consisted of feature importance extraction and exploring different methods to investigate different and unique ways to extract feature importance to construct a gene regulatory network.

The app visualizes a 3D graph of gene regulatory networks, allowing users to select a transcription factor and explore its neighboring interactions.

MIXED frustration fraction R across U(1), SU(2), SU(3) and SM gauge groups — lattice Monte Carlo + FSS (HGST E7)

Automated analysis tool for mutations in promoters, transcription factor binding sites, coding regions and protein domains in the context of gene regulatory networks.

BoNesisTools is a python package proposing bioinformatics tools for upstream and downstream analysis of BoNesis framework

An explainable inductive learning model on gene regulatory and toxicogenomic knowledge graph (under development...)

Autonomous 5-Sigma Agentic Engine for Mechanistic Inference in Spatial Transcriptomics. Employs a PINN-SINDy hybrid architecture and LangGraph orchestration to discover causal Gene Regulatory Networks (GRNs).