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Open Access

VGE: Gene-Disease Association by Variational Graph Embedding

Peng Han1, Xiangliang Zhang2( )
Department of Computer Science, University of Electronic Science and Technology of China, Chengdu 611731, China
College of Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
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Abstract

Disease-gene association, an important problem in the biomedical area, can be used to early intervene the treat of deadly diseases. Recently, models based on graph convolutional networks (GCNs) have outperformed previous linear models on predicting the new disease-gene associations, due to its strong capability to capture the relevance of disease and gene in the new semantic embedding space. However, a single embedding vector cannot informatively represent a disease or gene and cannot characterize the uncertainty of their features. We propose to learn a distribution for a disease or gene under the variational autoencoder framework, which enables disease-gene associations to be modeled by the Kullback-Leibler divergence. The experiment results show that our model outperforms the state-of-the-art models significantly in various metrics.

Keywords

graph convolutional network (GCN)disease-gene associationvariational antoencoder (VAE)

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International Journal of Crowd Science
Volume 8 Issue 2,
June 2024
Pages 95-99
DOI: 10.26599/IJCS.2024.9100004
Cite this article:
Han P, Zhang X. VGE: Gene-Disease Association by Variational Graph Embedding. International Journal of Crowd Science, 2024, 8(2): 95-99. https://doi.org/10.26599/IJCS.2024.9100004

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Received: 01 January 2024
Accepted: 06 January 2024
Published: 14 May 2024
© The author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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