PTHGRN

PTHGRN reconstructs hierarchical regulatory networks by integrating PPI, ChIP-seq, and gene expression data to infer conditional dependencies and identify drivers of transcriptional regulation.

Key Features:

• Diverse Data Integration: Combines PPI, ChIP-seq, and gene expression profiles for comprehensive regulatory network analysis.
• Gaussian Model with Partial Least Squares Regression: Applies a graphical Gaussian model and partial least squares regression to identify relationships among PTMs, TFs, epigenetic modifications, and gene expression patterns.

Scientific Applications:

• Transcriptional Regulation: Reveals underlying regulatory mechanisms in complex systems such as stem cells and cancerous tissues.

Methodology:

Constructs hierarchical networks using a graphical Gaussian model for conditional dependency inference and partial least squares regression to identify key drivers.