An Evolutionary Variable Neighborhood Search for Selecting Combinational Gene Signatures in Predicting Chemo-Response of Osteosarcoma
In genomic studies of cancers, identification of genetic biomarkers from analyzing microarray chip that interrogate thousands of genes is important for diagnosis and therapeutics. However, the commonly used statistical significance analysis can only provide information of each single gene, thus negl...
| Main Authors: | , , , |
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| Format: | Journal Article |
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Institute of Scientific Computing and Information
2010
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/42992 |
| _version_ | 1848756567603675136 |
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| author | Chan, Kit Yan Zhu, H. Aydin, M. Lau, C. |
| author_facet | Chan, Kit Yan Zhu, H. Aydin, M. Lau, C. |
| author_sort | Chan, Kit Yan |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In genomic studies of cancers, identification of genetic biomarkers from analyzing microarray chip that interrogate thousands of genes is important for diagnosis and therapeutics. However, the commonly used statistical significance analysis can only provide information of each single gene, thus neglecting the intrinsic interactions among genes. Therefore, methods aiming at combinational gene signatures are highly valuable. Supervised classification is an effective way to assess the function of a gene combination in differentiating various groups of samples. In this paper, an evolutionary variable neighborhood search (EVNS) that integrated the approaches of evolutionary algorithm and variable neighborhood search (VNS) is introduced.It consists of a population of solutions that evolution is performed by a variable neighborhood search operator, instead of the more usual reproduction operators, crossover and mutation used in evolutionary algorithms. It is an efficient search algorithm especially suitable for tremendous solution space. The proposed EVNS can simultaneously optimize the feature subset and the classifier through a common solution coding mechanism. This method was applied in searching the combinational gene signatures for predicting histologic response of chemotherapy on osteosarcoma patients, which is the most common malignant bone tumor in children. Cross-validation results show that EVNS outperforms the other existing approaches in classifying initial biopsy samples. |
| first_indexed | 2025-11-14T09:14:15Z |
| format | Journal Article |
| id | curtin-20.500.11937-42992 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:14:15Z |
| publishDate | 2010 |
| publisher | Institute of Scientific Computing and Information |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-429922017-01-30T15:03:41Z An Evolutionary Variable Neighborhood Search for Selecting Combinational Gene Signatures in Predicting Chemo-Response of Osteosarcoma Chan, Kit Yan Zhu, H. Aydin, M. Lau, C. Variable neighborhood search cancer gene evolutionary algorithm histologic response osteosarcoma In genomic studies of cancers, identification of genetic biomarkers from analyzing microarray chip that interrogate thousands of genes is important for diagnosis and therapeutics. However, the commonly used statistical significance analysis can only provide information of each single gene, thus neglecting the intrinsic interactions among genes. Therefore, methods aiming at combinational gene signatures are highly valuable. Supervised classification is an effective way to assess the function of a gene combination in differentiating various groups of samples. In this paper, an evolutionary variable neighborhood search (EVNS) that integrated the approaches of evolutionary algorithm and variable neighborhood search (VNS) is introduced.It consists of a population of solutions that evolution is performed by a variable neighborhood search operator, instead of the more usual reproduction operators, crossover and mutation used in evolutionary algorithms. It is an efficient search algorithm especially suitable for tremendous solution space. The proposed EVNS can simultaneously optimize the feature subset and the classifier through a common solution coding mechanism. This method was applied in searching the combinational gene signatures for predicting histologic response of chemotherapy on osteosarcoma patients, which is the most common malignant bone tumor in children. Cross-validation results show that EVNS outperforms the other existing approaches in classifying initial biopsy samples. 2010 Journal Article http://hdl.handle.net/20.500.11937/42992 Institute of Scientific Computing and Information fulltext |
| spellingShingle | Variable neighborhood search cancer gene evolutionary algorithm histologic response osteosarcoma Chan, Kit Yan Zhu, H. Aydin, M. Lau, C. An Evolutionary Variable Neighborhood Search for Selecting Combinational Gene Signatures in Predicting Chemo-Response of Osteosarcoma |
| title | An Evolutionary Variable Neighborhood Search for Selecting Combinational Gene Signatures in Predicting Chemo-Response of Osteosarcoma |
| title_full | An Evolutionary Variable Neighborhood Search for Selecting Combinational Gene Signatures in Predicting Chemo-Response of Osteosarcoma |
| title_fullStr | An Evolutionary Variable Neighborhood Search for Selecting Combinational Gene Signatures in Predicting Chemo-Response of Osteosarcoma |
| title_full_unstemmed | An Evolutionary Variable Neighborhood Search for Selecting Combinational Gene Signatures in Predicting Chemo-Response of Osteosarcoma |
| title_short | An Evolutionary Variable Neighborhood Search for Selecting Combinational Gene Signatures in Predicting Chemo-Response of Osteosarcoma |
| title_sort | evolutionary variable neighborhood search for selecting combinational gene signatures in predicting chemo-response of osteosarcoma |
| topic | Variable neighborhood search cancer gene evolutionary algorithm histologic response osteosarcoma |
| url | http://hdl.handle.net/20.500.11937/42992 |