Selecting informative genes from leukemia gene expression data using a hybrid approach for cancer classification
The development of microarray-based high-throughput gene profiling has led to the hope that this technology could provide an efficient and accurate means of diagnosing and classifying cancers. However, the large amount of data generated by microarrays requires effective selection of informative gene...
| Main Authors: | , , |
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| Format: | Book Section |
| Language: | English |
| Published: |
Springer Berlin Heidelberg
2007
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| Subjects: | |
| Online Access: | http://eprints.utm.my/9640/ http://eprints.utm.my/9640/1/MohdSaberiTan2007_SelectingInformativeGenesFromLeukemia.pdf |
| _version_ | 1848891900919021568 |
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| author | Mohamad, Mohd. Saberi Deris, Safaai Hashim, Siti Zaiton Mohd. |
| author_facet | Mohamad, Mohd. Saberi Deris, Safaai Hashim, Siti Zaiton Mohd. |
| author_sort | Mohamad, Mohd. Saberi |
| building | UTeM Institutional Repository |
| collection | Online Access |
| description | The development of microarray-based high-throughput gene profiling has led to the hope that this technology could provide an efficient and accurate means of diagnosing and classifying cancers. However, the large amount of data generated by microarrays requires effective selection of informative genes for cancer classification. Key issue that needs to be addressed is a selection of small number of informative genes that contribute to a disease from the thousands of genes measured on microarrays. This work deals with finding the small subset of informative genes from gene expression microarray data which maximize the classification accuracy. We introduce an improved version of hybrid of genetic algorithm and support vector machine for genes selection and classification. We show that the classification accuracy of the proposed approach is superior to a number of current state-of-the-art methods of one widely used benchmark dataset. The informative genes from the best subset are validated and verified by comparing them with the biological results produced from biology and computer scientist researchers in order to explore the biological plausibility. |
| first_indexed | 2025-11-15T21:05:19Z |
| format | Book Section |
| id | utm-9640 |
| institution | Universiti Teknologi Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T21:05:19Z |
| publishDate | 2007 |
| publisher | Springer Berlin Heidelberg |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utm-96402017-08-15T01:34:53Z http://eprints.utm.my/9640/ Selecting informative genes from leukemia gene expression data using a hybrid approach for cancer classification Mohamad, Mohd. Saberi Deris, Safaai Hashim, Siti Zaiton Mohd. QA75 Electronic computers. Computer science The development of microarray-based high-throughput gene profiling has led to the hope that this technology could provide an efficient and accurate means of diagnosing and classifying cancers. However, the large amount of data generated by microarrays requires effective selection of informative genes for cancer classification. Key issue that needs to be addressed is a selection of small number of informative genes that contribute to a disease from the thousands of genes measured on microarrays. This work deals with finding the small subset of informative genes from gene expression microarray data which maximize the classification accuracy. We introduce an improved version of hybrid of genetic algorithm and support vector machine for genes selection and classification. We show that the classification accuracy of the proposed approach is superior to a number of current state-of-the-art methods of one widely used benchmark dataset. The informative genes from the best subset are validated and verified by comparing them with the biological results produced from biology and computer scientist researchers in order to explore the biological plausibility. Springer Berlin Heidelberg 2007-04 Book Section PeerReviewed application/pdf en http://eprints.utm.my/9640/1/MohdSaberiTan2007_SelectingInformativeGenesFromLeukemia.pdf Mohamad, Mohd. Saberi and Deris, Safaai and Hashim, Siti Zaiton Mohd. (2007) Selecting informative genes from leukemia gene expression data using a hybrid approach for cancer classification. In: 3rd Kuala Lumpur International Conference on Biomedical Engineering 2006. Springer Berlin Heidelberg, pp. 528-532. ISBN 978-3-540-68016-1 http://dx.doi.org/10.1007/978-3-540-68017-8_133 doi : 10.1007/978-3-540-68017-8_133 |
| spellingShingle | QA75 Electronic computers. Computer science Mohamad, Mohd. Saberi Deris, Safaai Hashim, Siti Zaiton Mohd. Selecting informative genes from leukemia gene expression data using a hybrid approach for cancer classification |
| title | Selecting informative genes from leukemia gene expression data using a hybrid approach for cancer classification |
| title_full | Selecting informative genes from leukemia gene expression data using a hybrid approach for cancer classification |
| title_fullStr | Selecting informative genes from leukemia gene expression data using a hybrid approach for cancer classification |
| title_full_unstemmed | Selecting informative genes from leukemia gene expression data using a hybrid approach for cancer classification |
| title_short | Selecting informative genes from leukemia gene expression data using a hybrid approach for cancer classification |
| title_sort | selecting informative genes from leukemia gene expression data using a hybrid approach for cancer classification |
| topic | QA75 Electronic computers. Computer science |
| url | http://eprints.utm.my/9640/ http://eprints.utm.my/9640/ http://eprints.utm.my/9640/ http://eprints.utm.my/9640/1/MohdSaberiTan2007_SelectingInformativeGenesFromLeukemia.pdf |