In recent years, tasks of machine learning ranging from image processing &
audio/video analysis to natural language understanding have been transformed by
deep learning. The data content in all these scenarios are expressed via
Euclidean space. However, a considerable amount of application data is
structured in non-Euclidean space and is expressed as graphs, e.g. dealing with
complicated interactions & object interdependencies. Modelling physical
systems, learning molecular signatures, identifying protein interactions and
predicting diseases involve utilising a model that can adapt from graph data.
Graph neural networks (GNNs), specified as artificial-neural models, employ
message transmission between graph nodes to represent graph dependencies and
are primarily used in the non-Euclidean domain. Variants of GNN like Graph
Recurrent Networks (GRN), Graph Auto Encoder (GAE), Graph Convolution Networks
(GCN), Graph Adversarial Methods & Graph Reinforcement learning have exhibited
breakthrough productivity on a wide range of tasks, especially in the field of
bioinformatics, in recent years as a result of the rapid collection of
biological network data. Apart from presenting all existing GNN models,
mathematical analysis and comparison of the variants of all types of GNN have
been highlighted in this survey. Graph neural networks are investigated for
their potential real-world applications in various fields, focusing on
Bioinformatics. Furthermore, resources for evaluating graph neural network
models and accessing open-source code & benchmark data sets are included.
Ultimately, we provide some (seven) proposals for future research in this
rapidly evolving domain. GNNs have the potential to be an excellent tool for
solving a wide range of biological challenges in bioinformatics research, as
they are best represented as connected complex graphs.

深度学习在图神经网络中的非欧几里得空间的生物信息学研究中具有潜在的优势，能够解决与生物相关的复杂图形问题。