Prognosis of early cervical carcinoma using gene expression profiling

Cervical carcinoma remains a prime cause of cancer-related deaths in woman globally. Research into the prediction of the survivability for cervical cancer has been a challenge for researchers. Survival rates increase with earlier detection of cancer of the cervix. Cancer research and associated doma...

Full description

Bibliographic Details
Main Authors:	Zarzar, Mouayad, Razak, Eliza, Htike@Muhammad Yusof, Zaw Zaw, Yusof, Faridah
Format:	Conference or Workshop Item
Language:	English
Published:	2015
Subjects:	T Technology (General)
Online Access:	http://irep.iium.edu.my/48058/ http://irep.iium.edu.my/48058/ http://irep.iium.edu.my/48058/1/ID_124.pdf

id	iium-48058
recordtype	eprints
spelling	iium-480582017-03-15T08:33:33Z http://irep.iium.edu.my/48058/ Prognosis of early cervical carcinoma using gene expression profiling Zarzar, Mouayad Razak, Eliza Htike@Muhammad Yusof, Zaw Zaw Yusof, Faridah T Technology (General) Cervical carcinoma remains a prime cause of cancer-related deaths in woman globally. Research into the prediction of the survivability for cervical cancer has been a challenge for researchers. Survival rates increase with earlier detection of cancer of the cervix. Cancer research and associated domains have made significant strides over recent years. For example, cancer prognosis using machine learning techniques is now a promising area of research. Data mining and machine learning have found considerable application thru the use of microarray expression profiling inspection. Specifically, DNA chip gene expression technology is a promising tool that can identify cancerous cells in early phases of the disease by examining the gene expression of analyzed instances. Furthermore, microarray technology enables researchers to assay the expression of thousands of genes in parallel. In this paper, we present a Gaussian process regression model in order improve the prediction of survivability of patients with early cervical cancer. Additionally, stochastic proximity embedding (SPE) was applied to reducing the number of attributes by selecting the most informative genes of the input dataset. Consequently, the computational complexity was reduced and the performance of the proposed model was increased. Our results indicate that gene expression profiles combined with carefully chosen learning algorithms can predict patient survival for certain diseases. 2015 Conference or Workshop Item NonPeerReviewed application/pdf en http://irep.iium.edu.my/48058/1/ID_124.pdf Zarzar, Mouayad and Razak, Eliza and Htike@Muhammad Yusof, Zaw Zaw and Yusof, Faridah (2015) Prognosis of early cervical carcinoma using gene expression profiling. In: International Conference on Advances Technology in Telecommunication, Broadcasting, and Satellite, 26th-27th Sept. 2015, Jakarta, Indonesia. (In Press) http://telsatech.org/
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	T Technology (General)
spellingShingle	T Technology (General) Zarzar, Mouayad Razak, Eliza Htike@Muhammad Yusof, Zaw Zaw Yusof, Faridah Prognosis of early cervical carcinoma using gene expression profiling
description	Cervical carcinoma remains a prime cause of cancer-related deaths in woman globally. Research into the prediction of the survivability for cervical cancer has been a challenge for researchers. Survival rates increase with earlier detection of cancer of the cervix. Cancer research and associated domains have made significant strides over recent years. For example, cancer prognosis using machine learning techniques is now a promising area of research. Data mining and machine learning have found considerable application thru the use of microarray expression profiling inspection. Specifically, DNA chip gene expression technology is a promising tool that can identify cancerous cells in early phases of the disease by examining the gene expression of analyzed instances. Furthermore, microarray technology enables researchers to assay the expression of thousands of genes in parallel. In this paper, we present a Gaussian process regression model in order improve the prediction of survivability of patients with early cervical cancer. Additionally, stochastic proximity embedding (SPE) was applied to reducing the number of attributes by selecting the most informative genes of the input dataset. Consequently, the computational complexity was reduced and the performance of the proposed model was increased. Our results indicate that gene expression profiles combined with carefully chosen learning algorithms can predict patient survival for certain diseases.
format	Conference or Workshop Item
author	Zarzar, Mouayad Razak, Eliza Htike@Muhammad Yusof, Zaw Zaw Yusof, Faridah
author_facet	Zarzar, Mouayad Razak, Eliza Htike@Muhammad Yusof, Zaw Zaw Yusof, Faridah
author_sort	Zarzar, Mouayad
title	Prognosis of early cervical carcinoma using gene expression profiling
title_short	Prognosis of early cervical carcinoma using gene expression profiling
title_full	Prognosis of early cervical carcinoma using gene expression profiling
title_fullStr	Prognosis of early cervical carcinoma using gene expression profiling
title_full_unstemmed	Prognosis of early cervical carcinoma using gene expression profiling
title_sort	prognosis of early cervical carcinoma using gene expression profiling
publishDate	2015
url	http://irep.iium.edu.my/48058/ http://irep.iium.edu.my/48058/ http://irep.iium.edu.my/48058/1/ID_124.pdf
first_indexed	2023-09-18T21:08:17Z
last_indexed	2023-09-18T21:08:17Z
_version_	1777411080643936256

Prognosis of early cervical carcinoma using gene expression profiling

Similar Items