miRsyn

miRsyn infers synergistic interactions among microRNAs (miRNAs) in gene regulation using multiple-intervention causal inference to simulate experimental perturbations from observational expression data.

Key Features:

• miRNA synergism identification: Identifies synergistic interactions among microRNAs (miRNAs), including miRNA pairs and modules.
• Multiple-intervention causal inference: Implements a multiple-intervention causal inference framework to infer causal effects of joint miRNA perturbations.
• Simulation of interventions: Simulates multiple miRNA knockdown interventions using observational expression data.
• Sequence vs expression distinction: Distinguishes shared target relationships at the sequence level from synergistic effects observed at the expression level.
• Synergistic network construction: Constructs a synergistic network of miRNAs and characterizes its topological properties, including small-world features.
• Module detection and enrichment: Detects modules of synergistic miRNAs and identifies modules significantly enriched in breast cancer.
• Expression consistency analysis: Analyzes expression pattern consistency of inferred synergistic miRNA pairs.
• Comparative evaluation: Compares performance of the multiple-intervention causal inference method against single-intervention approaches.

Scientific Applications:

• Elucidating gene regulation: Elucidates synergistic miRNA interactions within gene regulatory networks.
• Disease-focused analysis: Investigates roles of miRNA synergism in complex human diseases.
• Breast cancer research: Identifies breast cancer–associated synergistic modules and provides insights into disease-specific regulatory mechanisms.
• Method assessment: Assesses and compares causal inference approaches for inferring miRNA synergy.
• Expression-level validation: Evaluates consistency of expression-level synergy among miRNA pairs that share targets at the sequence level.

Methodology:

Employs multiple-intervention causal inference to simulate multiple miRNA knockdown interventions from observational expression data, constructs a miRNA synergistic network, detects synergistic modules (including enrichment analysis in breast cancer), analyzes expression consistency of pairs, and compares results to single-intervention causal inference approaches.