Purpose

GDAP addresses the critical challenge of identifying novel gene-disease associations in biomedical research.

Problem Statement

Traditional experimental methods for discovering gene-disease relationships are:

• Time-consuming: Years of laboratory work
• Expensive: High costs for experimental validation
• Limited scope: Can only test a small number of hypotheses

Solution

GDAP provides a computational approach that:

• Accelerates discovery: Predicts associations in minutes
• Reduces costs: Prioritizes experimental targets
• Scales efficiently: Analyzes thousands of potential associations

Applications

Drug Discovery

• Identify novel therapeutic targets
• Prioritize drug development candidates
• Understand drug mechanism of action

Disease Research

• Elucidate disease mechanisms
• Discover genetic risk factors
• Understand disease progression

Personalized Medicine

• Identify patient-specific genetic factors
• Guide treatment selection
• Predict treatment response

Impact

GDAP enables researchers to:

1. Generate hypotheses about gene-disease relationships
2. Prioritize experiments based on prediction confidence
3. Validate predictions through targeted experiments
4. Accelerate research in precision medicine