Graph Signal Processing, Graph Neural Network and Graph Learning on Biological Data: A Systematic Review.

Li, R; Yuan, X; Radfar, M; Marendy, P; Ni, W; O'Brien, TJ; Casillas-Espinosa, PM

Graph Signal Processing, Graph Neural Network and Graph Learning on Biological Data: A Systematic Review.

Li, R Yuan, X

Radfar, M Marendy, P Ni, W

O'Brien, TJ Casillas-Espinosa, PM

Permalink

Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Reviews in Biomedical Engineering, 2023, 16, pp. 109-135
Issue Date:: 2023-10-26

Closed Access

	Filename	Description	Size
	Graph_Signal_Processing_Graph_Neural_Network_and_Graph_Learning_on_Biological_Data_A_Systematic_Review (1).pdf	Published version	4.02 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Li, R
dc.contributor.author	Yuan, X https://orcid.org/0000-0002-9167-1613
dc.contributor.author	Radfar, M
dc.contributor.author	Marendy, P
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X
dc.contributor.author	O'Brien, TJ
dc.contributor.author	Casillas-Espinosa, PM
dc.date.accessioned	2024-03-21T23:59:04Z
dc.date.available	2024-03-21T23:59:04Z
dc.date.issued	2023-10-26
dc.identifier.citation	IEEE Reviews in Biomedical Engineering, 2023, 16, pp. 109-135
dc.identifier.issn	1937-3333
dc.identifier.issn	1941-1189
dc.identifier.uri	http://hdl.handle.net/10453/176995
dc.description.abstract	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 assumptions for specific biological objectives. Finally, we discuss the biological application of graph analysis methods within the exhaustive literature collection, potentially providing insights for future research in the biological sciences.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation.ispartof	IEEE Reviews in Biomedical Engineering
dc.relation.isbasedon	10.1109/RBME.2021.3122522
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0903 Biomedical Engineering
dc.subject.classification	4003 Biomedical engineering
dc.subject.mesh	Humans
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Algorithms
dc.subject.mesh	Signal Processing, Computer-Assisted
dc.subject.mesh	Humans
dc.subject.mesh	Algorithms
dc.subject.mesh	Signal Processing, Computer-Assisted
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Humans
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Algorithms
dc.subject.mesh	Signal Processing, Computer-Assisted
dc.title	Graph Signal Processing, Graph Neural Network and Graph Learning on Biological Data: A Systematic Review.
dc.type	Journal Article
utslib.citation.volume	16
utslib.location.activity	United States
utslib.for	0903 Biomedical Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2024-03-21T23:59:02Z
pubs.publication-status	Published online
pubs.volume	16

Abstract:

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 assumptions for specific biological objectives. Finally, we discuss the biological application of graph analysis methods within the exhaustive literature collection, potentially providing insights for future research in the biological sciences.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/176995