dynUGENE

dynUGENE implements an uncertainty-aware extension of the dynGENIE3 algorithm to infer gene regulatory network architecture and simulate gene expression profiles for robust model selection.

Key Features:

• Uncertainty-aware inference: Incorporates uncertainty quantification into GRN inference to provide confidence measures for inferred regulatory interactions.
• Model selection: Integrates model selection functionality to identify optimal network representations from candidate models.
• Gene expression profile simulation: Simulates gene expression trajectories from inferred networks to validate models and explore hypothetical scenarios.
• dynGENIE3 extension: Builds upon the dynGENIE3 algorithm for GRN inference from high-throughput data.
• R package implementation: Provides the methods as an R package implementation for computational analysis.

Scientific Applications:

• Gene regulatory network analysis: Enables analysis of regulatory interactions in systems biology and genomics with uncertainty-aware inference.
• Simulation studies: Supports simulation-based hypothesis testing and experimental design through gene expression profile simulations.

Methodology:

Extension of the dynGENIE3 algorithm incorporating uncertainty quantification, integrated model selection, and simulation of gene expression profiles.