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...
| Main Authors: | , , , |
|---|---|
| Format: | Proceeding Paper |
| Language: | English |
| Published: |
2015
|
| Subjects: | |
| Online Access: | http://irep.iium.edu.my/48058/ http://irep.iium.edu.my/48058/1/ID_124.pdf |
| _version_ | 1848783242550837248 |
|---|---|
| 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 |
| building | IIUM Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-14T16:18:14Z |
| format | Proceeding Paper |
| id | iium-48058 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T16:18:14Z |
| publishDate | 2015 |
| recordtype | eprints |
| repository_type | Digital Repository |
| 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 Proceeding Paper 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/ |
| spellingShingle | T Technology (General) Zarzar, Mouayad Razak, Eliza Htike@Muhammad Yusof, Zaw Zaw Yusof, Faridah Prognosis of early cervical carcinoma using gene expression profiling |
| title | 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_short | Prognosis of early cervical carcinoma using gene expression profiling |
| title_sort | prognosis of early cervical carcinoma using gene expression profiling |
| topic | T Technology (General) |
| url | http://irep.iium.edu.my/48058/ http://irep.iium.edu.my/48058/ http://irep.iium.edu.my/48058/1/ID_124.pdf |