An improved parallelized mRMR for gene subset selection in cancer classification
DNA microarray technique has become a more attractive tool for cancer classification in the scientific and industrial fields. Based on the previous researchers, the conventional approach for cancer classification is primarily based on morphological appearance of the tumor. The limitations of this ap...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Insight Society
2017
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/21358/ http://umpir.ump.edu.my/id/eprint/21358/1/4.%20MRMR.pdf |
| _version_ | 1848821330329206784 |
|---|---|
| author | Kusairi, R.M. Kohbalan, Moorthy Habibollah, Haron Mohd Saberi, Mohamad Suhami, Napis Shahreen, Kasim |
| author_facet | Kusairi, R.M. Kohbalan, Moorthy Habibollah, Haron Mohd Saberi, Mohamad Suhami, Napis Shahreen, Kasim |
| author_sort | Kusairi, R.M. |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | DNA microarray technique has become a more attractive tool for cancer classification in the scientific and industrial fields. Based on the previous researchers, the conventional approach for cancer classification is primarily based on morphological appearance of the tumor. The limitations of this approach are bias in identify the tumors by expert and faced the difficulty in differentiate the cancer subtypes due to most cancers being highly related to the specific biological insight. Thus, this study propose an improved parallelized Minimum Redundancy Maximum Relevance (mRMR), which is a particularly fast feature selection method for finding a set of both relevant and complementary features. The mRMR can identify genes more relevance to biological context that leads to richer biological interpretations. The proposed method is expected to achieve accurate classification performance using small number of predictive genes when tested using two datasets from Cancer Genome Project and compared to previous methods. |
| first_indexed | 2025-11-15T02:23:38Z |
| format | Article |
| id | ump-21358 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T02:23:38Z |
| publishDate | 2017 |
| publisher | Insight Society |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-213582018-08-30T04:29:44Z http://umpir.ump.edu.my/id/eprint/21358/ An improved parallelized mRMR for gene subset selection in cancer classification Kusairi, R.M. Kohbalan, Moorthy Habibollah, Haron Mohd Saberi, Mohamad Suhami, Napis Shahreen, Kasim QA75 Electronic computers. Computer science RZ Other systems of medicine DNA microarray technique has become a more attractive tool for cancer classification in the scientific and industrial fields. Based on the previous researchers, the conventional approach for cancer classification is primarily based on morphological appearance of the tumor. The limitations of this approach are bias in identify the tumors by expert and faced the difficulty in differentiate the cancer subtypes due to most cancers being highly related to the specific biological insight. Thus, this study propose an improved parallelized Minimum Redundancy Maximum Relevance (mRMR), which is a particularly fast feature selection method for finding a set of both relevant and complementary features. The mRMR can identify genes more relevance to biological context that leads to richer biological interpretations. The proposed method is expected to achieve accurate classification performance using small number of predictive genes when tested using two datasets from Cancer Genome Project and compared to previous methods. Insight Society 2017 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/21358/1/4.%20MRMR.pdf Kusairi, R.M. and Kohbalan, Moorthy and Habibollah, Haron and Mohd Saberi, Mohamad and Suhami, Napis and Shahreen, Kasim (2017) An improved parallelized mRMR for gene subset selection in cancer classification. International Journal on Advanced Science, Engineering and Information Technology, 7 (4-2). pp. 1595-1600. ISSN 2088-5334. (Published) http://dx.doi.org/10.18517/ijaseit.7.4-2.3395 10.18517/ijaseit.7.4-2.3395 |
| spellingShingle | QA75 Electronic computers. Computer science RZ Other systems of medicine Kusairi, R.M. Kohbalan, Moorthy Habibollah, Haron Mohd Saberi, Mohamad Suhami, Napis Shahreen, Kasim An improved parallelized mRMR for gene subset selection in cancer classification |
| title | An improved parallelized mRMR for gene subset selection in cancer classification |
| title_full | An improved parallelized mRMR for gene subset selection in cancer classification |
| title_fullStr | An improved parallelized mRMR for gene subset selection in cancer classification |
| title_full_unstemmed | An improved parallelized mRMR for gene subset selection in cancer classification |
| title_short | An improved parallelized mRMR for gene subset selection in cancer classification |
| title_sort | improved parallelized mrmr for gene subset selection in cancer classification |
| topic | QA75 Electronic computers. Computer science RZ Other systems of medicine |
| url | http://umpir.ump.edu.my/id/eprint/21358/ http://umpir.ump.edu.my/id/eprint/21358/ http://umpir.ump.edu.my/id/eprint/21358/ http://umpir.ump.edu.my/id/eprint/21358/1/4.%20MRMR.pdf |