miRModuleNet

miRModuleNet identifies miRNA-mRNA regulatory modules from paired miRNA and mRNA expression profiles to reveal gene regulatory networks and disease-associated pathways.

Key Features:

• Detection of Regulatory Modules: Identifies miRNA-mRNA regulatory modules by grouping mRNAs correlated with specific miRNAs into a "star shape" configuration for analysis and visualization.
• Machine Learning Approach: Uses a machine learning strategy that groups and scores gene sets associated with each miRNA based on expression patterns.
• Scoring Procedure: Scores each module to evaluate significance and its contribution to classification tasks.
• Hierarchical Listing of Modules: Produces a hierarchical list of significant miRNA-mRNA regulatory modules for structured exploration.
• Validation and Functional Analysis: Validated on external datasets and applies functional enrichment analysis to interpret biological pathways implicated in cancer pathogenesis.

Scientific Applications:

• Cancer Research: Reveals miRNA-mRNA regulatory networks to aid understanding of molecular mechanisms of carcinogenesis and to identify potential biomarkers and therapeutic targets.
• Biomarker Identification: Highlights significant regulatory modules to pinpoint functional relationships between biomarkers.
• Pathway Analysis: Supports identification of essential pathways involved in cancer through functional enrichment analysis.

Methodology:

Requires paired miRNA and mRNA expression profiles as input; detects correlations between miRNAs and mRNAs; groups correlated mRNAs around each miRNA into "star shape" regulatory modules; applies a machine learning strategy to group and score gene sets; scores modules to assess significance and contribution to classification tasks; generates a hierarchical list of significant modules; validates results on external datasets and performs functional enrichment analysis.