GRNBoost2 and Arboreto: efficient and scalable inference of gene regulatory networks

Key Points

• To introduce GRNBoost2 and the Arboreto computational framework for fast, scalable inference of gene regulatory networks from large-scale gene expression datasets.
• Engineered Arboreto, a distributed computational framework designed to scale gene network inference algorithms.
• Implemented GRNBoost2, a gradient boosting-based inference method optimized for high computational speed and low memory usage across large transcriptomic profiles.
• Enables scalable inference of regulatory networks across massive datasets without prohibitive computational runtime.
• Demonstrates high computational efficiency and scalability compared to conventional network inference algorithms.