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A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions

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Published on: July 18, 2013

Network-Based Approaches for Disease-Gene Association Prediction Using Protein-Protein Interaction Networks.

Yoonbee Kim¹, Jong-Hoon Park¹, Young-Rae Cho^1,2

¹Division of Software, Yonsei University Mirae Campus, Wonju-si 26493, Gangwon-do, Korea.

International Journal of Molecular Sciences

|July 9, 2022

Summary

Network-based methods efficiently predict disease-gene associations by analyzing molecular networks. Integrative approaches and heterogeneous networks show superior performance for genetic disease research.

Keywords:

disease gene prioritization disease networks disease-gene associations heterogeneous networks protein-protein interaction networks

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Area of Science:

Computational biology
Genetics
Bioinformatics

Background:

Genome-wide association studies (GWAS) identify disease-related genomic regions but are resource-intensive.
Network-based computational approaches offer efficient disease-gene association prediction.
These methods assume disease-related genes cluster within molecular networks like protein-protein interaction (PPI) networks.

Purpose of the Study:

To survey network-based disease-gene association prediction methods.
To categorize these methods into graph-theoretic, machine learning, and integrated approaches.
To compare the performance of selected methods using a heterogeneous network.

Main Methods:

Constructed a heterogeneous network integrating PPI, disease, and known disease-gene association data.
Evaluated six network-based prediction methods.
Compared performance in scenarios with and without prior knowledge of disease-associated genes.

Main Results:

The integrative method HerGePred excelled when known disease genes were available.
The network propagation algorithm PRINCE performed best when known disease genes were absent.
Integrative and heterogeneous network approaches generally outperformed others.

Conclusions:

Network-based methods are valuable for disease-gene association prediction.
Integrative methods and heterogeneous networks enhance prediction accuracy.
Method selection may depend on the availability of prior genetic data.