Cover Image

Integrative machine learning analysis of multiple gene expression profiles in cervical cancer

Although most of the cervical cancer cases are reported to be closely related to the Human Papillomavirus (HPV) infection, there is a need to study genes that stand up differentially in the final actualization of cervical cancers following HPV infection. In this study, we proposed an integrative mac...

Full description

Saved in:
Bibliographic Details
Main Authors: Tan, Mei Sze, Chang, Siow Wee, Cheah, Phaik Leng, Yap, Hwa Jen
Format: Article
Published: PeerJ 2018
Subjects:
Q Science (General)
QH Natural history
R Medicine
TJ Mechanical engineering and machinery
Online Access:http://eprints.um.edu.my/20528/
https://doi.org/10.7717/peerj.5285
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.um.eprints.20528
record_format eprints
spelling my.um.eprints.205282019-02-27T03:24:32Z http://eprints.um.edu.my/20528/ Integrative machine learning analysis of multiple gene expression profiles in cervical cancer Tan, Mei Sze Chang, Siow Wee Cheah, Phaik Leng Yap, Hwa Jen Q Science (General) QH Natural history R Medicine TJ Mechanical engineering and machinery Although most of the cervical cancer cases are reported to be closely related to the Human Papillomavirus (HPV) infection, there is a need to study genes that stand up differentially in the final actualization of cervical cancers following HPV infection. In this study, we proposed an integrative machine learning approach to analyse multiple gene expression profiles in cervical cancer in order to identify a set of genetic markers that are associated with and may eventually aid in the diagnosis or prognosis of cervical cancers. The proposed integrative analysis is composed of three steps: namely, (i) gene expression analysis of individual dataset; (ii) meta-analysis of multiple datasets; and (iii) feature selection and machine learning analysis. As a result, 21 gene expressions were identified through the integrative machine learning analysis which including seven supervised and one unsupervised methods. A functional analysis with GSEA (Gene Set Enrichment Analysis) was performed on the selected 21-gene expression set and showed significant enrichment in a nine-potential gene expression signature, namely PEG3, SPON1, BTD and RPLP2 (upregulated genes) and PRDX3, COPB2, LSM3, SLC5A3 and AS1B (downregulated genes). PeerJ 2018 Article PeerReviewed Tan, Mei Sze and Chang, Siow Wee and Cheah, Phaik Leng and Yap, Hwa Jen (2018) Integrative machine learning analysis of multiple gene expression profiles in cervical cancer. PeerJ, 6. e5285. ISSN 2167-8359 https://doi.org/10.7717/peerj.5285 doi:10.7717/peerj.5285
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic Q Science (General)
QH Natural history
R Medicine
TJ Mechanical engineering and machinery
spellingShingle Q Science (General)
QH Natural history
R Medicine
TJ Mechanical engineering and machinery
Tan, Mei Sze
Chang, Siow Wee
Cheah, Phaik Leng
Yap, Hwa Jen
Integrative machine learning analysis of multiple gene expression profiles in cervical cancer
description Although most of the cervical cancer cases are reported to be closely related to the Human Papillomavirus (HPV) infection, there is a need to study genes that stand up differentially in the final actualization of cervical cancers following HPV infection. In this study, we proposed an integrative machine learning approach to analyse multiple gene expression profiles in cervical cancer in order to identify a set of genetic markers that are associated with and may eventually aid in the diagnosis or prognosis of cervical cancers. The proposed integrative analysis is composed of three steps: namely, (i) gene expression analysis of individual dataset; (ii) meta-analysis of multiple datasets; and (iii) feature selection and machine learning analysis. As a result, 21 gene expressions were identified through the integrative machine learning analysis which including seven supervised and one unsupervised methods. A functional analysis with GSEA (Gene Set Enrichment Analysis) was performed on the selected 21-gene expression set and showed significant enrichment in a nine-potential gene expression signature, namely PEG3, SPON1, BTD and RPLP2 (upregulated genes) and PRDX3, COPB2, LSM3, SLC5A3 and AS1B (downregulated genes).
format Article
author Tan, Mei Sze
Chang, Siow Wee
Cheah, Phaik Leng
Yap, Hwa Jen
author_facet Tan, Mei Sze
Chang, Siow Wee
Cheah, Phaik Leng
Yap, Hwa Jen
author_sort Tan, Mei Sze
title Integrative machine learning analysis of multiple gene expression profiles in cervical cancer
title_short Integrative machine learning analysis of multiple gene expression profiles in cervical cancer
title_full Integrative machine learning analysis of multiple gene expression profiles in cervical cancer
title_fullStr Integrative machine learning analysis of multiple gene expression profiles in cervical cancer
title_full_unstemmed Integrative machine learning analysis of multiple gene expression profiles in cervical cancer
title_sort integrative machine learning analysis of multiple gene expression profiles in cervical cancer
publisher PeerJ
publishDate 2018
url http://eprints.um.edu.my/20528/
https://doi.org/10.7717/peerj.5285
_version_ 1643691305641967616
score 13.211869

Similar Items