Li Rui, Yuan Xin, Radfar Mohsen, Marendy Peter, Ni Wei, O'Brien Terrence J, Casillas-Espinosa Pablo
IEEE Rev Biomed Eng. 2023;16:109-135. doi: 10.1109/RBME.2021.3122522. Epub 2023 Jan 5.
Graph networks can model data observed across different levels of biological systems that span from population graphs (with patients as network nodes) to molecular graphs that involve omics data. Graph-based approaches have shed light on decoding biological processes modulated by complex interactions. This paper systematically reviews graph-based analysis methods of Graph Signal Processing (GSP), Graph Neural Networks (GNNs) and graph topology inference, and their applications to biological data. This work focuses on the algorithms of graph-based approaches and the constructions of graph-based frameworks that are adapted to a broad range of biological data. We cover the Graph Fourier Transform and the graph filter developed in GSP, which provides tools to investigate biological signals in the graph domain that can potentially benefit from the underlying graph structures. We also review the node, graph, and interaction oriented applications of GNNs with inductive and transductive learning manners for various biological targets. As a key component of graph analysis, we provide a review of graph topology inference methods that incorporate assumptions for specific biological objectives. Finally, we discuss the biological application of graph analysis methods within this exhaustive literature collection, potentially providing insights for future research in biological sciences.
图网络可以对跨越不同生物系统层次的数据进行建模，这些层次从群体图（以患者为网络节点）到涉及组学数据的分子图。基于图的方法为解码由复杂相互作用调节的生物过程提供了线索。本文系统地综述了基于图的信号处理（GSP）、图神经网络（GNNs）和图拓扑推理的分析方法，以及它们在生物数据中的应用。这项工作重点关注基于图的方法的算法以及适用于广泛生物数据的基于图的框架的构建。我们涵盖了GSP中开发的图傅里叶变换和图滤波器，它们提供了在图域中研究生物信号的工具，这些信号可能受益于潜在的图结构。我们还综述了GNNs以归纳和转导学习方式针对各种生物目标的面向节点、图和相互作用的应用。作为图分析的关键组成部分，我们综述了结合特定生物学目标假设的图拓扑推理方法。最后，我们在这一详尽的文献集合中讨论图分析方法的生物学应用，可能为生物科学的未来研究提供见解。
