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Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

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MVGCN: data integration through multi-view graph convolutional network for predicting links in biomedical bipartite

Haitao Fu¹, Feng Huang¹, Xuan Liu¹

¹College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.

Bioinformatics (Oxford, England)

|September 9, 2021

Summary

This study introduces a novel multi-view graph convolution network (MVGCN) for predicting links in biomedical networks. The MVGCN framework demonstrates improved generalization capacity for identifying disease-related molecular mechanisms.

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Area of Science:

Biomedical informatics
Computational biology
Network science

Background:

Biomolecular interaction networks are crucial for understanding complex diseases.
Existing link prediction methods struggle with generalization across different networks and leveraging multi-feature bioentities.

Purpose of the Study:

To develop a novel framework for link prediction in biomedical bipartite networks.
To enhance the generalization capacity of prediction models by integrating multi-view information and bioentity features.

Main Methods:

A multi-view heterogeneous network (MVHN) was constructed by integrating similarity networks with the bipartite network.
A self-supervised learning strategy and a neighborhood information aggregation (NIA) layer were employed for node embedding updates.
A multi-view graph convolution network (MVGCN) framework was developed, combining embeddings from multiple NIA layers and views.

Main Results:

The proposed MVGCN framework achieved superior or comparable performance against baseline methods on six benchmark datasets.
The model demonstrated significant generalization capacity across different biomedical bipartite network tasks.
The framework effectively leverages multi-view information and bioentity features for improved link prediction.

Conclusions:

The MVGCN framework offers a robust and generalizable solution for link prediction in biomedical bipartite networks.
This approach aids in uncovering unobserved molecular mechanisms underlying complex human diseases.
The developed method has implications for disease diagnosis and treatment strategies.