Comparative study of feature selection method of microarray data for gene classification / Nurulhuda Ghazali … [et al.]
Recent advances in biotechnology such as microarray, offer the ability to measure the levels of expression of thousands of genes in parallel. Analysis of microarray data can provide understanding and insight into gene function and regulatory mechanisms. This analysis is crucial to identify and class...
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| Format: | Book Section |
| Language: | en |
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Faculty of Administrative Science and Policy Studies
2012
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| Online Access: | https://ir.uitm.edu.my/id/eprint/55268/1/55268.pdf https://ir.uitm.edu.my/id/eprint/55268/ |
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| author | Ghazali, Nurulhuda Hasan, Noraini Mohd Lip, Norliana Ghazali, Nur Hafizah Tajuddin, Mohammad Faridun Naim Saad, Puteh |
| author_facet | Ghazali, Nurulhuda Hasan, Noraini Mohd Lip, Norliana Ghazali, Nur Hafizah Tajuddin, Mohammad Faridun Naim Saad, Puteh |
| author_sort | Ghazali, Nurulhuda |
| building | Tun Abdul Razak Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknologi Mara |
| content_source | UiTM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Recent advances in biotechnology such as microarray, offer the ability to measure the levels of expression of thousands of genes in parallel. Analysis of microarray data can provide understanding and insight into gene function and regulatory mechanisms. This analysis is crucial to identify and classify cancer diseases. Recent technology in cancer classification is based on gene expression profile rather than on morphological appearance of the tumor. However, this task is made more difficult due to the noisy nature of microarray data and the overwhelming number of genes. Thus, it is an important issue to select a small subset of genes to represent thousands of genes in microarray data which is referred as informative genes. These informative genes will then be classified according to its appropriate classes. To achieve the best solution to the classification issue, we proposed an approach of minimum Redundancy-Maximum Relevance feature selection method together with Probabilistic Neural Network classifier. The minimum Redundancy-Maximum Relevance feature selection method is used to select the informative genes while the Probabilistic Neural Network classifier acts as the classifier. This approach has been tested on a well-known cancer dataset which is Leukemia. The results achieved shows that the gene selected had given high classification accuracy. This reduction of genes helps take out some burdens from biologist and better classification accuracy can be used widely to detect cancer in early stage. |
| format | Book Section |
| id | my.uitm.ir-55268 |
| institution | Universiti Teknologi Mara |
| language | en |
| publishDate | 2012 |
| publisher | Faculty of Administrative Science and Policy Studies |
| record_format | eprints |
| spelling | my.uitm.ir-552682022-03-11T09:01:51Z https://ir.uitm.edu.my/id/eprint/55268/ Comparative study of feature selection method of microarray data for gene classification / Nurulhuda Ghazali … [et al.] Ghazali, Nurulhuda Hasan, Noraini Mohd Lip, Norliana Ghazali, Nur Hafizah Tajuddin, Mohammad Faridun Naim Saad, Puteh Study and teaching. Research Genetics Biotechnology Recent advances in biotechnology such as microarray, offer the ability to measure the levels of expression of thousands of genes in parallel. Analysis of microarray data can provide understanding and insight into gene function and regulatory mechanisms. This analysis is crucial to identify and classify cancer diseases. Recent technology in cancer classification is based on gene expression profile rather than on morphological appearance of the tumor. However, this task is made more difficult due to the noisy nature of microarray data and the overwhelming number of genes. Thus, it is an important issue to select a small subset of genes to represent thousands of genes in microarray data which is referred as informative genes. These informative genes will then be classified according to its appropriate classes. To achieve the best solution to the classification issue, we proposed an approach of minimum Redundancy-Maximum Relevance feature selection method together with Probabilistic Neural Network classifier. The minimum Redundancy-Maximum Relevance feature selection method is used to select the informative genes while the Probabilistic Neural Network classifier acts as the classifier. This approach has been tested on a well-known cancer dataset which is Leukemia. The results achieved shows that the gene selected had given high classification accuracy. This reduction of genes helps take out some burdens from biologist and better classification accuracy can be used widely to detect cancer in early stage. Faculty of Administrative Science and Policy Studies 2012 Book Section PeerReviewed text en https://ir.uitm.edu.my/id/eprint/55268/1/55268.pdf Comparative study of feature selection method of microarray data for gene classification / Nurulhuda Ghazali … [et al.]. (2012) In: 3rd International Conference on Public Policy and Social Science ( ICOPS 2012). Faculty of Administrative Science and Policy Studies, Melaka, pp. 71-85. ISBN 978-967-11354-5-7 |
| spellingShingle | Study and teaching. Research Genetics Biotechnology Ghazali, Nurulhuda Hasan, Noraini Mohd Lip, Norliana Ghazali, Nur Hafizah Tajuddin, Mohammad Faridun Naim Saad, Puteh Comparative study of feature selection method of microarray data for gene classification / Nurulhuda Ghazali … [et al.] |
| title | Comparative study of feature selection method of microarray data for gene classification / Nurulhuda Ghazali … [et al.] |
| title_full | Comparative study of feature selection method of microarray data for gene classification / Nurulhuda Ghazali … [et al.] |
| title_fullStr | Comparative study of feature selection method of microarray data for gene classification / Nurulhuda Ghazali … [et al.] |
| title_full_unstemmed | Comparative study of feature selection method of microarray data for gene classification / Nurulhuda Ghazali … [et al.] |
| title_short | Comparative study of feature selection method of microarray data for gene classification / Nurulhuda Ghazali … [et al.] |
| title_sort | comparative study of feature selection method of microarray data for gene classification / nurulhuda ghazali … [et al.] |
| topic | Study and teaching. Research Genetics Biotechnology |
| url | https://ir.uitm.edu.my/id/eprint/55268/1/55268.pdf https://ir.uitm.edu.my/id/eprint/55268/ |
| url_provider | http://ir.uitm.edu.my/ |