PORTIA

PORTIA infers gene regulatory networks (GRNs) from gene expression data to identify transcriptional regulatory interactions and cellular transcriptional states.

Key Features:

• Robust Precision Matrix Estimation: Uses a novel robust precision matrix estimation method to infer GRNs, yielding high accuracy and orders-of-magnitude faster runtimes compared to state-of-the-art methods.
• Validation and Benchmarking: Validated on benchmark datasets including DREAM and MERLIN+P to assess performance against existing GRN inference methods.
• Novel Scoring Metric: Implements a graph-theoretical scoring metric to evaluate inferred network structure and quality.

Scientific Applications:

• Transcriptional Regulation Analysis: Reconstruction of regulatory interactions underlying cellular responses to external stimuli based on gene expression changes.
• Systems Biology: Modeling of transcriptional state spaces and regulatory network structure in systems-level studies.
• Genomics: Inference of gene regulatory interactions from gene expression data for genomics research.
• Personalized Medicine: Investigation of regulatory mechanisms relevant to disease progression and individual-specific transcriptional regulation.

Methodology:

Robust precision matrix estimation for GRN inference and a graph-theoretical scoring metric for network evaluation; benchmarking performed on DREAM and MERLIN+P datasets.