iRF LOOPy

iRF LOOPy

iRF_LOOPpy is a high-performance Python package for inferring Gene Regulatory Networks (GRNs) using Iterative Random Forest (iRF). This tool leverages machine learning to predict regulatory interactions between genes, generating Predictive Expression Networks (PENs).

Features

• Efficient Network Inference: Uses iRF, an advanced tree-based model, to infer regulatory relationships from gene expression data.
• MPI-Based Task Farm: Implements a dynamic task allocation system for parallelized computation, reducing idle times compared to traditional batch queuing.
• Streamlined Workflow: Includes preprocessing, processing, and post-processing steps for ease of use.
• Consolidated Output: Unlike previous versions, all model results are stored in a single output file, improving data management.

Citation:

If you use this work, please cite iRF-LOOPy:

Lane, Matthew, Lagergren, John, Townsend, Alice, Alvarez, Christiane, and Jacobson, Daniel. iRF-LOOPy. Computer Software. https://github.com/Jacobson-CompSysBio/GRN-LOOPy/releases/tag/irf_loopy_v0.1.0. 29 May. 2025. Web. doi:10.11578/dc.20250730.1.