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: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Indonesian Society for Knowledge and Human Development
2017
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| Online Access: | http://psasir.upm.edu.my/id/eprint/60822/ http://psasir.upm.edu.my/id/eprint/60822/1/An%20improved%20parallelized%20mRMR%20for%20gene%20subset%20selection%20in%20cancer%20classification.pdf |
| _version_ | 1848854282804133888 |
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| author | Mohammad Kusairi, Rohani Moorthy, Kohbalan Haron, Habibollah Mohamad, Mohd Saberi Napis, Suhaimi Kasim, Shahreen |
| author_facet | Mohammad Kusairi, Rohani Moorthy, Kohbalan Haron, Habibollah Mohamad, Mohd Saberi Napis, Suhaimi Kasim, Shahreen |
| author_sort | Mohammad Kusairi, Rohani |
| building | UPM 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 | 2025-11-15T11:07:24Z |
| format | Article |
| id | upm-60822 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T11:07:24Z |
| publishDate | 2017 |
| publisher | Indonesian Society for Knowledge and Human Development |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-608222019-03-26T09:28:26Z http://psasir.upm.edu.my/id/eprint/60822/ An improved parallelized mRMR for gene subset selection in cancer classification Mohammad Kusairi, Rohani Moorthy, Kohbalan Haron, Habibollah Mohamad, Mohd Saberi Napis, Suhaimi Kasim, Shahreen 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. Indonesian Society for Knowledge and Human Development 2017 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/60822/1/An%20improved%20parallelized%20mRMR%20for%20gene%20subset%20selection%20in%20cancer%20classification.pdf Mohammad Kusairi, Rohani and Moorthy, Kohbalan and Haron, Habibollah and Mohamad, Mohd Saberi and Napis, Suhaimi and Kasim, Shahreen (2017) An improved parallelized mRMR for gene subset selection in cancer classification. International Journal on Advanced Science, Engineering and Information Technology, 7 (4-2). 1595 - 1600. ISSN 2088-5334; ESSN: 2460-6952 10.18517/ijaseit.7.4-2.3395 |
| spellingShingle | Mohammad Kusairi, Rohani Moorthy, Kohbalan Haron, Habibollah Mohamad, Mohd Saberi Napis, Suhaimi Kasim, Shahreen 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 |
| url | http://psasir.upm.edu.my/id/eprint/60822/ http://psasir.upm.edu.my/id/eprint/60822/ http://psasir.upm.edu.my/id/eprint/60822/1/An%20improved%20parallelized%20mRMR%20for%20gene%20subset%20selection%20in%20cancer%20classification.pdf |