Visualization of Biomedical Data

O'Donoghue, S; Baldi, B; Clark, S; Darling, A; Hogan, J; Kaur, S; Maier-Hein, L; McCarthy, D; Moore, W; Stenau, E; Swedlow, J; Vuong, J; Procter, J

Visualization of Biomedical Data

O'Donoghue, S Baldi, B Clark, S Darling, A

Hogan, J Kaur, S Maier-Hein, L McCarthy, D Moore, W Stenau, E Swedlow, J Vuong, J Procter, J

Permalink

Publication Type:: Journal Article
Citation:: 2018, 6, pp. e26896v1
Issue Date:: 2018-04-26

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download full textAdobe PDF (14.11 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	O'Donoghue, S
dc.contributor.author	Baldi, B
dc.contributor.author	Clark, S
dc.contributor.author	Darling, A https://orcid.org/0000-0003-2397-7925
dc.contributor.author	Hogan, J
dc.contributor.author	Kaur, S
dc.contributor.author	Maier-Hein, L
dc.contributor.author	McCarthy, D
dc.contributor.author	Moore, W
dc.contributor.author	Stenau, E
dc.contributor.author	Swedlow, J
dc.contributor.author	Vuong, J
dc.contributor.author	Procter, J
dc.date.accessioned	2022-04-01T03:55:11Z
dc.date.available	2022-04-01T03:55:11Z
dc.date.issued	2018-04-26
dc.identifier.citation	2018, 6, pp. e26896v1
dc.identifier.issn	2167-9843
dc.identifier.uri	http://hdl.handle.net/10453/155867
dc.description.abstract	The rapid increase in volume and complexity of biomedical data requires changes in research, communication, training, and clinical practices. This includes learning how to effectively integrate automated analysis with high-data-density visualizations that clearly express complex phenomena. In this review, we summarize key principles and resources from data visualization research that address this difficult challenge. We then survey how visualization is being used in a selection of emerging biomedical research areas, including: 3D genomics, single-cell RNA-seq, the protein structure universe, phosphoproteomics, augmented-reality surgery, and metagenomics. While specific areas need highly tailored visualization tools, there are common visualization challenges that can be addressed with general methods and strategies. Unfortunately, poor visualization practices are also common; however, there are good prospects for improvements and innovations that will revolutionize how we see and think about our data. We outline initiatives aimed at fostering these improvements via better tools, peer-to-peer learning, and interdisciplinary collaboration with computer scientists, science communicators, and graphic designers.
dc.language	en
dc.relation.isbasedon	10.7287/peerj.preprints.26896v1
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Visualization of Biomedical Data
dc.type	Journal Article
utslib.citation.volume	6
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Strength - ithree - Institute of Infection, Immunity and Innovation
utslib.copyright.status	open_access	*
dc.date.updated	2022-04-01T03:55:05Z
pubs.publication-status	Published
pubs.volume	6

Abstract:

The rapid increase in volume and complexity of biomedical data requires changes in research, communication, training, and clinical practices. This includes learning how to effectively integrate automated analysis with high-data-density visualizations that clearly express complex phenomena. In this review, we summarize key principles and resources from data visualization research that address this difficult challenge. We then survey how visualization is being used in a selection of emerging biomedical research areas, including: 3D genomics, single-cell RNA-seq, the protein structure universe, phosphoproteomics, augmented-reality surgery, and metagenomics. While specific areas need highly tailored visualization tools, there are common visualization challenges that can be addressed with general methods and strategies. Unfortunately, poor visualization practices are also common; however, there are good prospects for improvements and innovations that will revolutionize how we see and think about our data. We outline initiatives aimed at fostering these improvements via better tools, peer-to-peer learning, and interdisciplinary collaboration with computer scientists, science communicators, and graphic designers.

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

http://hdl.handle.net/10453/155867