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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

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Published on: October 13, 2023

Edge-level multi-constraint graph pattern matching with lung cancer knowledge graph.

Houdie Tu¹, Lei Li^2,3, Zhenchao Tao^4,5

¹School of Artificial Intelligence, Hefei University of Technology, Hefei, China.

Frontiers in Big Data

|March 28, 2025

Summary

This summary is machine-generated.

This study introduces novel graph pattern matching (GPM) algorithms, TEM and THM, utilizing the Monte Carlo method for lung cancer knowledge graphs. These methods enhance efficiency and address uncertainty in medical data analysis.

Keywords:

Monte Carlo method graph pattern matching lung cancer knowledge graph multi-constranint probability graph

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

Bioinformatics
Computational Biology
Medical Informatics

Background:

Traditional Graph Pattern Matching (GPM) excels in complex network analysis but is limited in medical data applications.
Lung cancer knowledge graphs present unique challenges due to data complexity and uncertainty.

Purpose of the Study:

To adapt and enhance GPM techniques for effective analysis of lung cancer knowledge graphs.
To introduce novel algorithms that improve efficiency and accuracy in retrieving relevant patterns from medical graph data.

Main Methods:

Development of an edge-level multi-constraint GPM algorithm (TEM) using the Monte Carlo method.
Proposal of a multi-constraint hologram pattern matching algorithm (THM) incorporating Monte Carlo methods for both nodes and edges.
Application of these algorithms to a lung cancer knowledge graph.

Main Results:

Experimental validation confirmed the effectiveness and efficiency of the TEM algorithm.
The proposed methods demonstrated significant improvements in efficiency compared to existing algorithms.
Successful handling of uncertainty within the lung cancer knowledge graph.

Conclusions:

The developed TEM and THM algorithms offer a robust solution for GPM in lung cancer knowledge graphs.
These Monte Carlo-based approaches significantly advance the efficiency and applicability of GPM in medical data analysis.
The study highlights the potential of GPM for complex biomedical data interpretation.