Graph Neural Networks and Their Current Applications in Bioinformatics - Zhang, Xiao-Meng; Liang, Li; Liu, Lin; Tang, Ming-Jing (Frontiers in Genetics, 2021)
Graph neural networks (GNNs), as a branch of deep learning in non-Euclidean space, perform particularly well in various tasks that process graph structure data. With the rapid accumulation of biological network data, GNNs have also become an important tool in bioinformatics. In this research, a systematic survey of GNNs and their advances in bioinformatics is presented from multiple perspectives. We first introduce some commonly used GNN models and their basic principles. Then, three representative tasks are proposed based on the three levels of structural information that can be learned by GNNs: node classification, link prediction and graph generation. Meanwhile, according to the specific applications for various omics data, we categorize and discuss the related studies in three aspects: disease prediction, drug discovery and biomedical imaging. Based on the analysis, we outlook on the shortcomings of current studies and point out their developing prospect. Although GNNs have achieved excellent results in many biological tasks at present, it still faces challenges in terms of low-quality data processing, methodology and interpretability, and has a long road ahead. We believe that GNNs are potentially a wonderful method that solves various biological problems in bioinformatics research.