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A novel graph attention adversarial network for predicting disease-related associations
Journal article   Peer reviewed

A novel graph attention adversarial network for predicting disease-related associations

Jinli Zhang, Zongli Jiang, Xiaohua Hu and Bo Song
Methods (San Diego, Calif.), v 179, pp 81-88
01 Jul 2020
DOI: https://doi.org/10.1016/j.ymeth.2020.05.010
PMID: 32446956
Featured in Collection :   UN Sustainable Development Goals @ Drexel

Abstract

Adversarial regularization Disease-related association Gaph convolution networks Network representation
•We take full advantage of disease-RNA networks fusing with meta paths.•We apply GCN integrating with attention mechanism.•We propose a novel adversarial regularized graph attention network model.•We conduct extensive experiments for the comparison of our model and recent models. Identifying complex human diseases at molecular level is very helpful, especially in diseases diagnosis, therapy, prognosis and monitoring. Accumulating evidences demonstrated that RNAs are playing important roles in identifying various complex human diseases. However, the amount of verified disease-related RNAs is still little while many of their biological experiments are very time-consuming and labor-intensive. Therefore, researchers have instead been seeking to develop effective computational algorithms to predict associations between diseases and RNAs. In this paper, we propose a novel model called Graph Attention Adversarial Network (GAAN) for the potential disease-RNA association prediction. To our best knowledge, we are among the pioneers to integrate successfully both the state-of-the-art graph convolutional networks (GCNs) and attention mechanism in our model for the prediction of disease-RNA associations. Comparing to other disease-RNA association prediction methods, GAAN is novel in conducting the computations from the aspect of global structure of disease-RNA network with graph embedding while integrating features of local neighborhoods with the attention mechanism. Moreover, GAAN uses adversarial regularization to further discover feature representation distribution of the latent nodes in disease-RNA networks. GAAN also benefits from the efficiency of deep model for the computation of big associations networks. To evaluate the performance of GAAN, we conduct experiments on networks of diseases associating with two different RNAs: MicroRNAs (miRNAs) and Long non-coding RNAs (lncRNAs). Comparisons of GAAN with several popular baseline methods on disease-RNA networks show that our novel model outperforms others by a wide margin in predicting potential disease-RNAs associations.

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22 citations in Web of Science
26 citations in Scopus

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UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#3 Good Health and Well-Being

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Collaboration types
Domestic collaboration
International collaboration
Web of Science research areas
Biochemical Research Methods
Biochemistry & Molecular Biology
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