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Can I read Graph Signal Processing, Graph Neural Network and Graph Learning on Biological Data: A Systematic Review on EtoBox?
Graph Signal Processing, Graph Neural Network and Graph Learning on Biological Data: A Systematic Review by Rui Li; Xin Yuan; Mohsen Radfar; Peter Marendy; Wei Ni; Terrence J. O'Brien; Pablo Casillas-Espinosa is a Engineering article available to read on EtoBox.
What is Graph Signal Processing, Graph Neural Network and Graph Learning on Biological Data: A Systematic Review about?
Graph networks can model the data observed across different levels of biological systems that span from the population graph (with patients as network nodes) to the molecular graphs that involve omics data. Graph-based approaches have shed light on decoding biological processes modulated by complex interactions. This paper systematically reviews the graph-based analysis methods, including Graph Signal Processing (GSP), Graph Neural Network (GNN), and graph topology inference methods, and their applications to biological data. This work focuses on the algorithms of the graph-based approaches and the constructions of the graph-based frameworks that are adapted to the broad range of biological data. We cover the Graph Fourier Transform and the graph filter developed in GSP, which provides tools to investigate biological networks in the graph domain that can potentially benefit from the underlying graph structure. We also review the node, graph, and interaction oriented GNN architecture with inductive and transductive learning manners for various biological objectives. As the key component of graph analysis, we provide a review of the graph topology inference methods that incorporate a
Who reads Graph Signal Processing, Graph Neural Network and Graph Learning on Biological Data: A Systematic Review?
It is typically read by researchers, students, and practitioners in Engineering.
- Author
- Rui Li; Xin Yuan; Mohsen Radfar; Peter Marendy; Wei Ni; Terrence J. O'Brien; Pablo Casillas-Espinosa
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Published
- 2023
- Language
- EN
- Field
- Engineering (Physical Sciences)