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LR-GNN: a graph neural network based on link representation for predicting molecular associations.

Chuanze Kang¹, Han Zhang¹, Zhuo Liu¹

¹College of Artificial Intelligence, Nankai University, Tongyan Road, 300350, Tianjin, China.

Briefings in Bioinformatics

|December 10, 2021

Summary

We introduce a novel Graph Neural Network (GNN) method, LR-GNN, for accurately predicting molecular associations in biomedical networks. This link representation approach enhances the discovery of crucial biological relationships.

Keywords:

biomedical networks graph neural network link representation molecular association prediction

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Area of Science:

Computational Biology
Bioinformatics
Network Science

Background:

Understanding molecular associations is vital for deciphering biological processes and functions.
Graph Neural Networks (GNNs) show promise in discovering biologically significant molecular relationships.
Accurate link representation learning for predicting molecular associations remains a challenge.

Purpose of the Study:

To present a novel GNN-based method, LR-GNN, for identifying potential molecular associations.
To improve the accuracy of predicting molecular relationships through effective link representation.

Main Methods:

Developed LR-GNN, a GNN utilizing a Graph Convolutional Network (GCN)-encoder for node embedding.
Designed a novel propagation rule to capture node embeddings across GCN layers for link representation (LR).
Implemented a layer-wise fusing rule to combine LRs from all layers for enhanced prediction accuracy.

Main Results:

LR-GNN demonstrated superior performance over state-of-the-art methods on four diverse biomedical networks (lncRNA-disease, miRNA-disease, PPI, DDI).
The method achieved robust performance across different network types.
Case studies validated LR-GNN's capability in predicting previously unknown molecular associations.

Conclusions:

LR-GNN effectively predicts molecular associations by leveraging link representation learning.
The proposed method offers a robust and accurate approach for biomedical network analysis.
Visualization confirmed the effectiveness of the link representation strategy.