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

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

Published on: October 13, 2023

A knowledge-based approach for predicting gene-disease associations.

Hongyi Zhou¹, Jeffrey Skolnick¹

¹School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA.

Bioinformatics (Oxford, England)

|June 11, 2016

Summary

This summary is machine-generated.

We developed Know-GENE, a novel method using gene mutual information and protein networks to predict gene-disease associations, significantly improving accuracy over existing approaches for early disease intervention.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Next-generation sequencing enables rapid gene variation identification, crucial for understanding disease associations.
Genome-wide association studies (GWAS) have identified numerous genes linked to common diseases.
Accurate gene-disease association prediction is vital for early disease intervention and therapeutic target discovery.

Purpose of the Study:

To develop a knowledge-based computational approach for predicting gene-disease associations.
To leverage existing gene-disease association data and protein-protein interaction networks.
To improve the accuracy and scope of gene-disease association predictions for both common and rare diseases.

Main Methods:

Derived gene-gene mutual information from known gene-disease association data.
Integrated mutual information with protein-protein interaction networks using boosted tree regression.
Developed the Know-GENE prediction method.

Main Results:

Know-GENE achieved a 65.0% recall rate within the top 100 predicted genes for 960 diseases.
This significantly outperforms the state-of-the-art random walking method (37.9% recall).
The improvement is attributed to the incorporation of knowledge-based mutual information.

Conclusions:

The Know-GENE approach demonstrates superior performance in predicting gene-disease associations.
This method offers a valuable tool for identifying potential therapeutic targets and advancing precision medicine.
Predictions are publicly available, facilitating further research and application.