Arboreto

Arboreto infers gene regulatory networks from large-scale gene expression datasets, including single-cell RNA sequencing, using scalable gradient-boosting algorithms.

Key Features:

• Scalability and Efficiency: Handles extensive gene expression datasets and supports computational scalability and efficiency.
• Integration with GRNBoost2 and GENIE3: Implements GRNBoost2 and an enhanced implementation of GENIE3, both based on gradient boosting for regulatory network inference.
• Compliance with GENIE3 Architecture: Adheres to the GENIE3 architecture to ensure methodological compatibility in regulatory network inference.

Scientific Applications:

• Systems Biology: Infers genome-wide regulatory interactions to analyze system-level gene regulation from expression data.
• Developmental Biology: Reconstructs regulatory programs underlying developmental processes from expression profiles.
• Personalized Medicine: Enables inference of patient- or sample-specific regulatory networks from expression datasets.
• Regulatory Gene Identification: Identifies key regulatory genes driving expression patterns in large-scale datasets.
• Cellular Response and Genetic Interaction Analysis: Supports analysis of cellular responses and exploration of genetic interactions at a granular level.

Methodology:

Applies gradient boosting via GRNBoost2 and GENIE3 to model non-linear relationships between gene expression profiles and predict regulatory links, with implementations designed for scalability and computational efficiency.