A survey of neural network-based cancer prediction models from microarray data

Simple item page
dc.contributor.authorDaoud, Maisaen_NZ
dc.contributor.authorMayo, Michaelen_NZ
dc.date.accessioned2019-06-30T23:49:12Z
dc.date.available2019en_NZ
dc.date.available2019-06-30T23:49:12Z
dc.date.issued2019en_NZ
dc.description.abstractNeural networks are powerful tools used widely for building cancer prediction models from microarray data. We review the most recently proposed models to highlight the roles of neural networks in predicting cancer from gene expression data. We identified articles published between 2013–2018 in scientific databases using keywords such as cancer classification, cancer analysis, cancer prediction, cancer clustering and microarray data. Analyzing the studies reveals that neural network methods have been either used for filtering (data engineering) the gene expressions in a prior step to prediction; predicting the existence of cancer, cancer type or the survivability risk; or for clustering unlabeled samples. This paper also discusses some practical issues that can be considered when building a neural network-based cancer prediction model. Results indicate that the functionality of the neural network determines its general architecture. However, the decision on the number of hidden layers, neurons, hypermeters and learning algorithm is made using trail-and-error techniques.en_NZ
dc.format.mimetypeapplication/pdf
dc.identifier.citationDaoud, M., & Mayo, M. (2019). A survey of neural network-based cancer prediction models from microarray data. Artificial Intelligence in Medicine, 97, 204–214. https://doi.org/10.1016/j.artmed.2019.01.006en
dc.identifier.doi10.1016/j.artmed.2019.01.006en_NZ
dc.identifier.eissn1873-2860en_NZ
dc.identifier.issn0933-3657en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/12654
dc.language.isoen
dc.publisherElsevieren_NZ
dc.relation.isPartOfArtificial Intelligence in Medicineen_NZ
dc.rights© 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).
dc.subjectcomputer scienceen_NZ
dc.subjectcancer prediction modelsen_NZ
dc.subjectneural networksen_NZ
dc.subjectclassificationen_NZ
dc.subjectclusteringen_NZ
dc.subjectfilteringen_NZ
dc.subjectMachine learning
dc.titleA survey of neural network-based cancer prediction models from microarray dataen_NZ
dc.typeJournal Article
pubs.begin-page204
pubs.elements-id235648
pubs.end-page214
pubs.notesQA: EBSCOen_NZ
pubs.organisational-group/Waikato
pubs.organisational-group/Waikato/2024 PBRF
pubs.organisational-group/Waikato/DHEC
pubs.organisational-group/Waikato/DHEC/2024 PBRF - DHEC
pubs.organisational-group/Waikato/DHEC/SCMS
pubs.organisational-group/Waikato/DHEC/SCMS/2024 PBRF - SCMS
pubs.publication-statusPublisheden_NZ
pubs.user.infoMayo, Michael (mmayo@waikato.ac.nz)
pubs.volume97en_NZ
uow.verification.statusverified
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
1-s2.0-S0933365717305067-main.pdf
Size:
1.22 MB
Format:
Adobe Portable Document Format
Description:
Published version
Thumbnail Image
Name: 1-s2.0-S0933365717305067-main.pdf
Size: 1.22 MB
Kind: Adobe PDF
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
Research Commons Deposit Agreement 2017.pdf
Size:
188.11 KB
Format:
Adobe Portable Document Format
Description:
Name: Research Commons Deposit Agreement 2017.pdf
Size: 188.11 KB
Kind: Adobe PDF
Collections
Computing and Mathematical Sciences Papers