Virtual reality for the observation of oncology models (VROOM): immersive analytics for oncology patient cohorts.
Lau, CW
Qu, Z
Draper, D
Quan, R
Braytee, A
Bluff, A
Zhang, D
Johnston, A
Kennedy, PJ
Simoff, S
Nguyen, QV
Catchpoole, D
- Publisher:
- NATURE PORTFOLIO
- Publication Type:
- Journal Article
- Citation:
- Sci Rep, 2022, 12, (1), pp. 11337
- Issue Date:
- 2022-07-05
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Full metadata record
Field | Value | Language |
---|---|---|
dc.contributor.author | Lau, CW | |
dc.contributor.author | Qu, Z | |
dc.contributor.author | Draper, D | |
dc.contributor.author | Quan, R | |
dc.contributor.author |
Braytee, A https://orcid.org/0000-0003-2561-6496 |
|
dc.contributor.author |
Bluff, A https://orcid.org/0000-0003-3215-279X |
|
dc.contributor.author | Zhang, D | |
dc.contributor.author |
Johnston, A https://orcid.org/0000-0001-8306-7507 |
|
dc.contributor.author | Kennedy, PJ | |
dc.contributor.author | Simoff, S | |
dc.contributor.author | Nguyen, QV | |
dc.contributor.author |
Catchpoole, D https://orcid.org/0000-0001-5836-1413 |
|
dc.date.accessioned | 2023-01-31T00:05:39Z | |
dc.date.available | 2022-06-24 | |
dc.date.available | 2023-01-31T00:05:39Z | |
dc.date.issued | 2022-07-05 | |
dc.identifier.citation | Sci Rep, 2022, 12, (1), pp. 11337 | |
dc.identifier.issn | 2045-2322 | |
dc.identifier.issn | 2045-2322 | |
dc.identifier.uri | http://hdl.handle.net/10453/165632 | |
dc.description.abstract | The significant advancement of inexpensive and portable virtual reality (VR) and augmented reality devices has re-energised the research in the immersive analytics field. The immersive environment is different from a traditional 2D display used to analyse 3D data as it provides a unified environment that supports immersion in a 3D scene, gestural interaction, haptic feedback and spatial audio. Genomic data analysis has been used in oncology to understand better the relationship between genetic profile, cancer type, and treatment option. This paper proposes a novel immersive analytics tool for cancer patient cohorts in a virtual reality environment, virtual reality to observe oncology data models. We utilise immersive technologies to analyse the gene expression and clinical data of a cohort of cancer patients. Various machine learning algorithms and visualisation methods have also been deployed in VR to enhance the data interrogation process. This is supported with established 2D visual analytics and graphical methods in bioinformatics, such as scatter plots, descriptive statistical information, linear regression, box plot and heatmap into our visualisation. Our approach allows the clinician to interrogate the information that is familiar and meaningful to them while providing them immersive analytics capabilities to make new discoveries toward personalised medicine. | |
dc.format | Electronic | |
dc.language | eng | |
dc.publisher | NATURE PORTFOLIO | |
dc.relation.ispartof | Sci Rep | |
dc.relation.isbasedon | 10.1038/s41598-022-15548-1 | |
dc.rights | info:eu-repo/semantics/openAccess | |
dc.subject.mesh | Augmented Reality | |
dc.subject.mesh | Feedback | |
dc.subject.mesh | Humans | |
dc.subject.mesh | Neoplasms | |
dc.subject.mesh | Research Design | |
dc.subject.mesh | Virtual Reality | |
dc.subject.mesh | Humans | |
dc.subject.mesh | Neoplasms | |
dc.subject.mesh | Research Design | |
dc.subject.mesh | Feedback | |
dc.subject.mesh | Virtual Reality | |
dc.subject.mesh | Augmented Reality | |
dc.subject.mesh | Augmented Reality | |
dc.subject.mesh | Feedback | |
dc.subject.mesh | Humans | |
dc.subject.mesh | Neoplasms | |
dc.subject.mesh | Research Design | |
dc.subject.mesh | Virtual Reality | |
dc.title | Virtual reality for the observation of oncology models (VROOM): immersive analytics for oncology patient cohorts. | |
dc.type | Journal Article | |
utslib.citation.volume | 12 | |
utslib.location.activity | England | |
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/Strength - CHT - Health Technologies | |
pubs.organisational-group | /University of Technology Sydney/Strength - HCTD - Human Centred Technology Design | |
pubs.organisational-group | /University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute | |
pubs.organisational-group | /University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering | |
pubs.organisational-group | /University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science | |
pubs.organisational-group | /University of Technology Sydney/Centre for Health Technologies (CHT) | |
utslib.copyright.status | open_access | * |
dc.date.updated | 2023-01-31T00:05:17Z | |
pubs.issue | 1 | |
pubs.publication-status | Published online | |
pubs.volume | 12 | |
utslib.citation.issue | 1 |
Abstract:
The significant advancement of inexpensive and portable virtual reality (VR) and augmented reality devices has re-energised the research in the immersive analytics field. The immersive environment is different from a traditional 2D display used to analyse 3D data as it provides a unified environment that supports immersion in a 3D scene, gestural interaction, haptic feedback and spatial audio. Genomic data analysis has been used in oncology to understand better the relationship between genetic profile, cancer type, and treatment option. This paper proposes a novel immersive analytics tool for cancer patient cohorts in a virtual reality environment, virtual reality to observe oncology data models. We utilise immersive technologies to analyse the gene expression and clinical data of a cohort of cancer patients. Various machine learning algorithms and visualisation methods have also been deployed in VR to enhance the data interrogation process. This is supported with established 2D visual analytics and graphical methods in bioinformatics, such as scatter plots, descriptive statistical information, linear regression, box plot and heatmap into our visualisation. Our approach allows the clinician to interrogate the information that is familiar and meaningful to them while providing them immersive analytics capabilities to make new discoveries toward personalised medicine.
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