A Robust Hybrid Approach Based on Estimation of Distribution Algorithm and Support Vector Machine for Hunting Candidate Disease Genes
Microarray data are high dimension with high noise ratio and relatively small sample size, which makes it a challenge to use microarray data to identify candidate disease genes. Here, we have presented a hybrid method that combines estimation of distribution algorithm with support vector machine for...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2013/393570 |
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| _version_ | 1832551885472530432 |
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| author | Li Li Hongmei Chen Chang Liu Fang Wang Fangfang Zhang Lihua Bai Yihan Chen Luying Peng |
| author_facet | Li Li Hongmei Chen Chang Liu Fang Wang Fangfang Zhang Lihua Bai Yihan Chen Luying Peng |
| author_sort | Li Li |
| collection | DOAJ |
| description | Microarray data are high dimension with high noise ratio and relatively small sample size, which makes it a challenge to use microarray data to identify candidate disease genes. Here, we have presented a hybrid method that combines estimation of distribution algorithm with support vector machine for selection of key feature genes. We have benchmarked the method using the microarray data of both diffuse B cell lymphoma and colon cancer to demonstrate its performance for identifying key features from the profile data of high-dimension gene expression. The method was compared with a probabilistic model based on genetic algorithm and another hybrid method based on both genetics algorithm and support vector machine. The results showed that the proposed method provides new computational strategy for hunting candidate disease genes from the profile data of disease gene expression. The selected candidate disease genes may help to improve the diagnosis and treatment for diseases. |
| format | Article |
| id | doaj-art-7598edbf3e0d425faa2ff6fe97b9fcdf |
| institution | Kabale University |
| issn | 1537-744X |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-7598edbf3e0d425faa2ff6fe97b9fcdf2025-02-03T06:00:07ZengWileyThe Scientific World Journal1537-744X2013-01-01201310.1155/2013/393570393570A Robust Hybrid Approach Based on Estimation of Distribution Algorithm and Support Vector Machine for Hunting Candidate Disease GenesLi Li0Hongmei Chen1Chang Liu2Fang Wang3Fangfang Zhang4Lihua Bai5Yihan Chen6Luying Peng7Devision of Medical Genetics, Tongji University School of Medicine, Shanghai 200092, ChinaDevision of Medical Genetics, Tongji University School of Medicine, Shanghai 200092, ChinaDevision of Medical Genetics, Tongji University School of Medicine, Shanghai 200092, ChinaDevision of Medical Genetics, Tongji University School of Medicine, Shanghai 200092, ChinaDevision of Medical Genetics, Tongji University School of Medicine, Shanghai 200092, ChinaKey Lab for Basic Research in Cardiology, Ministry of Education, Tongji University, Shanghai 200092, ChinaKey Lab for Basic Research in Cardiology, Ministry of Education, Tongji University, Shanghai 200092, ChinaDevision of Medical Genetics, Tongji University School of Medicine, Shanghai 200092, ChinaMicroarray data are high dimension with high noise ratio and relatively small sample size, which makes it a challenge to use microarray data to identify candidate disease genes. Here, we have presented a hybrid method that combines estimation of distribution algorithm with support vector machine for selection of key feature genes. We have benchmarked the method using the microarray data of both diffuse B cell lymphoma and colon cancer to demonstrate its performance for identifying key features from the profile data of high-dimension gene expression. The method was compared with a probabilistic model based on genetic algorithm and another hybrid method based on both genetics algorithm and support vector machine. The results showed that the proposed method provides new computational strategy for hunting candidate disease genes from the profile data of disease gene expression. The selected candidate disease genes may help to improve the diagnosis and treatment for diseases.http://dx.doi.org/10.1155/2013/393570 |
| spellingShingle | Li Li Hongmei Chen Chang Liu Fang Wang Fangfang Zhang Lihua Bai Yihan Chen Luying Peng A Robust Hybrid Approach Based on Estimation of Distribution Algorithm and Support Vector Machine for Hunting Candidate Disease Genes The Scientific World Journal |
| title | A Robust Hybrid Approach Based on Estimation of Distribution Algorithm and Support Vector Machine for Hunting Candidate Disease Genes |
| title_full | A Robust Hybrid Approach Based on Estimation of Distribution Algorithm and Support Vector Machine for Hunting Candidate Disease Genes |
| title_fullStr | A Robust Hybrid Approach Based on Estimation of Distribution Algorithm and Support Vector Machine for Hunting Candidate Disease Genes |
| title_full_unstemmed | A Robust Hybrid Approach Based on Estimation of Distribution Algorithm and Support Vector Machine for Hunting Candidate Disease Genes |
| title_short | A Robust Hybrid Approach Based on Estimation of Distribution Algorithm and Support Vector Machine for Hunting Candidate Disease Genes |
| title_sort | robust hybrid approach based on estimation of distribution algorithm and support vector machine for hunting candidate disease genes |
| url | http://dx.doi.org/10.1155/2013/393570 |
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