EGAD

EGAD analyzes gene networks using guilt-by-association extended with degree-based evaluations to predict gene function and assess how networks group known gene sets.

Key Features:

• Efficiency and Speed: Implements computationally efficient algorithms to enable exhaustive evaluations of large gene networks.
• Guilt-by-Association Methodology: Extends guilt-by-association to predict novel gene group members by evaluating functional associations within networks.
• High-Throughput Capability: Supports analysis of large-scale gene networks against hundreds or thousands of gene sets.
• Performance Assessment: Quantifies how well a network groups known gene sets and assesses the impact of generic predictions on overall performance.
• Versatility in Evaluation: Performs fast assessments using both random and real functional gene sets.

Scientific Applications:

• Gene function prediction: Predicts novel members of gene groups based on network associations.
• Network evaluation: Evaluates the accuracy with which a gene network clusters known gene sets.
• Assessment of generic predictions: Determines the influence of generic predictions on network performance.

Methodology:

Implements computationally efficient algorithms and extends guilt-by-association with degree-based evaluations, performing assessments using random and real functional gene sets to evaluate grouping and the impact of generic predictions.