Null Result Detection GNN System

A graph neural network (GNN) system designed to automatically detect and validate null results in scientific literature.

Project Overview

This system uses advanced graph neural networks and Bayesian validation techniques to identify, analyze, and validate null results in scientific research papers. By constructing knowledge graphs from research literature and applying specialized detection algorithms, the system can help researchers identify patterns and validate findings across multiple studies.

Key Components

1. Knowledge Graph Construction

• ResearchGraphBuilder: Constructs a knowledge graph from scientific papers, connecting hypotheses, methodologies, and results.

2. Null Detection Engine

• Graph Autoencoder: Learns embeddings of research relationships to identify potential null result patterns.
• Bayesian Validation Layer: Applies Bayesian inference to validate null results with statistical rigor.
• Parallel Analysis Core: Processes multiple research paths simultaneously for efficient detection.

3. Eliza Framework Integration

• Plugins for seamless integration with the Eliza research framework.
• APIs for extending functionality and connecting with external research databases.

Installation

pip install -r requirements.txt

Usage

See the documentation in the docs/ directory for detailed usage instructions and examples.

References

• PMC6412612 methodology for null result validation
• Wagenmakers' criteria for Bayesian hypothesis testing