Case-based retrieval framework for gene expression data

Anaissi, A; Goyal, M; Catchpoole, DR; Braytee, A; Kennedy, PJ

Case-based retrieval framework for gene expression data

Anaissi, A Goyal, M

Catchpoole, DR Braytee, A

Kennedy, PJ

Permalink

Publication Type:: Journal Article
Citation:: Cancer Informatics, 2015, 14 pp. 21 - 31
Issue Date:: 2015-03-19

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Published VersionAdobe PDF (1.94 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Anaissi, A	en_US
dc.contributor.author	Goyal, M https://orcid.org/0000-0003-2853-9393	en_US
dc.contributor.author	Catchpoole, DR	en_US
dc.contributor.author	Braytee, A https://orcid.org/0000-0003-2561-6496	en_US
dc.contributor.author	Kennedy, PJ https://orcid.org/0000-0001-7837-3171	en_US
dc.date.available	2015-01-22	en_US
dc.date.issued	2015-03-19	en_US
dc.identifier.citation	Cancer Informatics, 2015, 14 pp. 21 - 31	en_US
dc.identifier.uri	http://hdl.handle.net/10453/35241
dc.description.abstract	© the authors, publisher and licensee Libertas academica Limited. Background: The process of retrieving similar cases in a case-based reasoning system is considered a big challenge for gene expression data sets. The huge number of gene expression values generated by microarray technology leads to complex data sets and similarity measures for high-dimensional data are problematic. Hence, gene expression similarity measurements require numerous machine-learning and data-mining techniques, such as feature selection and dimensionality reduction, to be incorporated into the retrieval process.Methods: This article proposes a case-based retrieval framework that uses a k-nearest-neighbor classifier with a weighted-feature-based similarity to retrieve previously treated patients based on their gene expression profiles. Results: The herein-proposed methodology is validated on several data sets: a childhood leukemia data set collected from The Children’s Hospital at Westmead, as well as the Colon cancer, the National Cancer Institute (NCI), and the Prostate cancer data sets. Results obtained by the proposed framework in retrieving patients of the data sets who are similar to new patients are as follows: 96% accuracy on the childhood leukemia data set, 95% on the NCI data set, 93% on the Colon cancer data set, and 98% on the Prostate cancer data set. Conclusion: The designed case-based retrieval framework is an appropriate choice for retrieving previous patients who are similar to a new patient, on the basis of their gene expression data, for better diagnosis and treatment of childhood leukemia. Moreover, this framework can be applied to other gene expression data sets using some or all of its steps.	en_US
dc.relation.ispartof	Cancer Informatics	en_US
dc.relation.isbasedon	10.4137/CIN.S22371	en_US
dc.subject.classification	Bioinformatics	en_US
dc.title	Case-based retrieval framework for gene expression data	en_US
dc.type	Journal Article
utslib.citation.volume	14	en_US
utslib.for	1112 Oncology and Carcinogenesis	en_US
pubs.embargo.period	Not known	en_US
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 Computer Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	14	en_US

Abstract:

© the authors, publisher and licensee Libertas academica Limited. Background: The process of retrieving similar cases in a case-based reasoning system is considered a big challenge for gene expression data sets. The huge number of gene expression values generated by microarray technology leads to complex data sets and similarity measures for high-dimensional data are problematic. Hence, gene expression similarity measurements require numerous machine-learning and data-mining techniques, such as feature selection and dimensionality reduction, to be incorporated into the retrieval process.Methods: This article proposes a case-based retrieval framework that uses a k-nearest-neighbor classifier with a weighted-feature-based similarity to retrieve previously treated patients based on their gene expression profiles. Results: The herein-proposed methodology is validated on several data sets: a childhood leukemia data set collected from The Children’s Hospital at Westmead, as well as the Colon cancer, the National Cancer Institute (NCI), and the Prostate cancer data sets. Results obtained by the proposed framework in retrieving patients of the data sets who are similar to new patients are as follows: 96% accuracy on the childhood leukemia data set, 95% on the NCI data set, 93% on the Colon cancer data set, and 98% on the Prostate cancer data set. Conclusion: The designed case-based retrieval framework is an appropriate choice for retrieving previous patients who are similar to a new patient, on the basis of their gene expression data, for better diagnosis and treatment of childhood leukemia. Moreover, this framework can be applied to other gene expression data sets using some or all of its steps.

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

http://hdl.handle.net/10453/35241