INfORM

INfORM identifies and prioritizes response gene modules from transcriptome data using consensus network inference to detect statistically and biologically significant modules.

Key Features:

• Comprehensive module detection: Identifies biologically meaningful response modules from consensus gene networks inferred from transcriptome data.
• Consensus network inference: Integrates results from multiple network inference algorithms to produce consensus gene networks.
• Statistical and biological significance evaluation: Evaluates and selects gene modules based on statistical significance and biological relevance.
• Gene prioritization by functional and topological roles: Evaluates and prioritizes key genes within modules according to their functional annotations and network topology.
• Network-based analysis of expression data: Interprets responsive modules within gene expression data using network-based approaches.

Scientific Applications:

• Systems biology: Characterizes network-level responses and module organization in complex biological systems.
• Genomics: Supports interpretation of transcriptome datasets through module detection and network inference.
• Gene regulation studies: Aids elucidation of functional roles and interactions among genes within regulatory networks.
• Disease mechanism analysis: Enables investigation of network perturbations underlying disease processes.
• Therapeutic target discovery: Assists identification and prioritization of candidate therapeutic targets within response modules.

Methodology:

Consensus gene networks are inferred from transcriptome data by integrating multiple network inference algorithms; identified modules and genes are evaluated and prioritized based on statistical significance, biological relevance, and functional/topological roles.