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

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GLADIATOR: a global approach for elucidating disease modules.

Yael Silberberg¹, Martin Kupiec¹, Roded Sharan²

¹Department of Molecular Microbiology and Biotechnology, Tel Aviv University, Tel Aviv, Israel.

Genome Medicine

|May 28, 2017

Summary

This summary is machine-generated.

We developed GLADIATOR, a novel method for predicting disease modules by analyzing protein-protein interactions and phenotypes across many diseases simultaneously. This approach enhances understanding of disease mechanisms and identifies potential therapeutic targets.

Keywords:

Disease gene prediction Disease modules Disease pathways Graphs and networks Hyperinsulinism Protein-protein interaction network

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

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Published on: June 21, 2018

Area of Science:

Computational biology
Systems biology
Genomics and bioinformatics

Background:

Understanding the genetic basis of diseases is crucial in biology and medicine.
Protein-protein interaction networks are valuable for identifying disease mechanisms and modules.
Existing methods often focus on individual diseases, limiting a broader understanding.

Purpose of the Study:

To develop a global method for simultaneous prediction of multiple disease modules.
To leverage phenotypic similarity across diseases for a comprehensive view of disease modules.
To improve the identification and understanding of disease mechanisms through network analysis.

Main Methods:

Introduced GLADIATOR (GLobal Approach for DIsease AssociaTed mOdule Reconstruction), a novel computational method.
Utilized a gold-standard phenotypic similarity measure for a pan-disease perspective.
Employed a simulated annealing algorithm to optimize module similarity against phenotypic similarity across hundreds of diseases.

Main Results:

Predicted disease modules demonstrated high agreement with known disease-related proteins.
Modules showed significant functional coherence and enrichment with curated pathways, outperforming prior methods.
Analysis revealed the diverse roles of shared proteins in related diseases and suggested novel proteins for hyperinsulinism.

Conclusions:

GLADIATOR effectively integrates protein and phenotype data to predict disease modules across multiple diseases.
The predicted modules are functionally coherent and align better with biological knowledge than those from disease-centric methods.
The GLADIATOR tool is available for download, facilitating further research in disease mechanism elucidation.