iRF_LOOPy

iRF_LOOPy is a high-performance, open-source scientific software package designed to infer Gene Regulatory Networks (GRNs) from gene expression data using Iterative Random Forest (iRF). It predicts regulatory interactions and generates Predictive Expression Networks (PENs), leveraging machine learning and parallel computing to enhance inference efficiency.

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: All model results are stored in a single output file, improving data management.

Developers:
Matthew Lane, John Lagergren, Alice Townsend, Christiane Alvarez, Daniel Jacobson

DOI: 10.11578/dc.20250730.1
Release Date: 2025-05-29
Code ID: 156236
License: BSD 3-clause “New” or “Revised” License
Programming Language: Python
Version: 0.1.0
Project Type: Open Source, Publicly Available Repository
Software Type: Scientific
Country of Origin: United States
Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN, USA

Repository and additional documentation can be accessed via GitHub - Jacobson-CompSysBio/GRN-LOOPy.