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Related Experiment Video

Updated: Oct 16, 2025

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

Identifying disease-gene associations using a convolutional neural network-based model by embedding a biological

Wonjun Choi¹, Hyunju Lee¹

¹School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Buk-gu, Gwangju, Republic of Korea.

|October 15, 2021

Summary

This study introduces a new computational model, KGED, to predict cancer-related genes more efficiently. The model improves the identification of crucial genes for understanding disease mechanisms and discovering new treatments.

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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

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Area of Science:

Computational biology
Genomics
Bioinformatics

Background:

Identifying genes linked to human diseases is crucial but experimentally intensive.
Computational methods are increasingly vital for predicting candidate genes in complex diseases like cancer.

Purpose of the Study:

To develop and evaluate a novel convolutional neural network-based knowledge graph-embedding model (KGED) for inferring gene-gene relationships.
To identify central genes associated with specific cancer types using network analysis.

Main Methods:

Utilized a biological knowledge graph with entity descriptions to train the KGED model.
Generated cancer-specific gene-interaction networks based on KGED-inferred relationships.
Applied network centrality measures (betweenness, closeness, degree, eigenvector) to rank genes.

Main Results:

The KGED model demonstrated superior performance in predicting cancer-related genes compared to existing methods.
Successfully constructed and analyzed gene-interaction networks for prostate, breast, and lung cancers.
Identified key central genes highly correlated with cancer through network analysis.

Conclusions:

The KGED model effectively infers gene-gene interactions, aiding in the prediction of cancer-related genes.
Inferred gene interactions can accelerate research into pathogenic mechanisms and disease treatment discovery.