Graph Neural Networks in Biomedical Data: A Review

You      Li; Guiyang      Zhang; Pan      Wang; Zuo-Guo      Yu; Guohua      Huang
Abstract

With the development of sequencing technology, various forms of biomedical data, including genomics, transcriptomics, proteomics, microbiomics, and metabolomics data, are increasingly emerging. These data are an external manifestation of cell activity and mechanism. How to deeply analyze these data is critical to uncovering and understanding the nature of life. Due to the heterogeneousness and complexity of these data, it is a vastly challenging task for traditional machine learning to deal with it. Over the recent ten years, a new machine learning framework called graph neural networks (GNNs) has been proposed. The graph is a very powerful tool to represent a complex system. The GNNs is becoming a key to open the mysterious door of life. In this paper, we focused on summarizing state-ofthe- art GNNs algorithms (GraphSAGE, graph convolutional network, graph attention network, graph isomorphism network and graph auto-encoder), briefly introducing the main principles behind them. We also reviewed some applications of the GNNs to the area of biomedicine, and finally discussed the possible developing direction of GNNs in the future.
Keywords: Graph neural networks, graph convolutional network, graph attention network, graph auto-encoder, biomedical data, disease prediction.
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Cite As
Current Bioinformatics

Graph Neural Networks in Biomedical Data: A Review

Abstract
