RRHGE: A Novel Approach to Classify the Estrogen Receptor Based Breast Cancer Subtypes
Background. Breast cancer is the most common type of cancer among females with a high mortality rate. It is essential to classify the estrogen receptor based breast cancer subtypes into correct subclasses, so that the right treatments can be applied to lower the mortality rate. Using gene signatures...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/362141 |
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| author | Ashish Saini Jingyu Hou Wanlei Zhou |
| author_facet | Ashish Saini Jingyu Hou Wanlei Zhou |
| author_sort | Ashish Saini |
| collection | DOAJ |
| description | Background. Breast cancer is the most common type of cancer among females with a high mortality rate. It is essential to classify the estrogen receptor based breast cancer subtypes into correct subclasses, so that the right treatments can be applied to lower the mortality rate. Using gene signatures derived from gene interaction networks to classify breast cancers has proven to be more reproducible and can achieve higher classification performance. However, the interactions in the gene interaction network usually contain many false-positive interactions that do not have any biological meanings. Therefore, it is a challenge to incorporate the reliability assessment of interactions when deriving gene signatures from gene interaction networks. How to effectively extract gene signatures from available resources is critical to the success of cancer classification. Methods. We propose a novel method to measure and extract the reliable (biologically true or valid) interactions from gene interaction networks and incorporate the extracted reliable gene interactions into our proposed RRHGE algorithm to identify significant gene signatures from microarray gene expression data for classifying ER+ and ER− breast cancer samples. Results. The evaluation on real breast cancer samples showed that our RRHGE algorithm achieved higher classification accuracy than the existing approaches. |
| format | Article |
| id | doaj-art-5e92b51c1ed24f77b4c02a352aaedd56 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-5e92b51c1ed24f77b4c02a352aaedd562025-08-20T03:34:33ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/362141362141RRHGE: A Novel Approach to Classify the Estrogen Receptor Based Breast Cancer SubtypesAshish Saini0Jingyu Hou1Wanlei Zhou2School of Information Technology, Deakin University, 221 Burwood Highway, Melbourne, VIC 3125, AustraliaSchool of Information Technology, Deakin University, 221 Burwood Highway, Melbourne, VIC 3125, AustraliaSchool of Information Technology, Deakin University, 221 Burwood Highway, Melbourne, VIC 3125, AustraliaBackground. Breast cancer is the most common type of cancer among females with a high mortality rate. It is essential to classify the estrogen receptor based breast cancer subtypes into correct subclasses, so that the right treatments can be applied to lower the mortality rate. Using gene signatures derived from gene interaction networks to classify breast cancers has proven to be more reproducible and can achieve higher classification performance. However, the interactions in the gene interaction network usually contain many false-positive interactions that do not have any biological meanings. Therefore, it is a challenge to incorporate the reliability assessment of interactions when deriving gene signatures from gene interaction networks. How to effectively extract gene signatures from available resources is critical to the success of cancer classification. Methods. We propose a novel method to measure and extract the reliable (biologically true or valid) interactions from gene interaction networks and incorporate the extracted reliable gene interactions into our proposed RRHGE algorithm to identify significant gene signatures from microarray gene expression data for classifying ER+ and ER− breast cancer samples. Results. The evaluation on real breast cancer samples showed that our RRHGE algorithm achieved higher classification accuracy than the existing approaches.http://dx.doi.org/10.1155/2014/362141 |
| spellingShingle | Ashish Saini Jingyu Hou Wanlei Zhou RRHGE: A Novel Approach to Classify the Estrogen Receptor Based Breast Cancer Subtypes The Scientific World Journal |
| title | RRHGE: A Novel Approach to Classify the Estrogen Receptor Based Breast Cancer Subtypes |
| title_full | RRHGE: A Novel Approach to Classify the Estrogen Receptor Based Breast Cancer Subtypes |
| title_fullStr | RRHGE: A Novel Approach to Classify the Estrogen Receptor Based Breast Cancer Subtypes |
| title_full_unstemmed | RRHGE: A Novel Approach to Classify the Estrogen Receptor Based Breast Cancer Subtypes |
| title_short | RRHGE: A Novel Approach to Classify the Estrogen Receptor Based Breast Cancer Subtypes |
| title_sort | rrhge a novel approach to classify the estrogen receptor based breast cancer subtypes |
| url | http://dx.doi.org/10.1155/2014/362141 |
| work_keys_str_mv | AT ashishsaini rrhgeanovelapproachtoclassifytheestrogenreceptorbasedbreastcancersubtypes AT jingyuhou rrhgeanovelapproachtoclassifytheestrogenreceptorbasedbreastcancersubtypes AT wanleizhou rrhgeanovelapproachtoclassifytheestrogenreceptorbasedbreastcancersubtypes |