Pathway-based analysis with Support Vector Machine (SVM-LASSO) for gene selection and classification
Genomic knowledge has become a popular research field in bioinformatics biological process that providing further biological process information. Many methods have been done to address the issues of high data throughput due to increased use of microarray technology. However, it is still not able to...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
INSIGHT - Indonesian Society for Knowledge and Human Development
2017
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| Online Access: | http://psasir.upm.edu.my/id/eprint/62655/ http://psasir.upm.edu.my/id/eprint/62655/1/GENOMIC.pdf |
| _version_ | 1848854646588702720 |
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| author | Nasrudin, Nurul Athirah Chan, Weng Howe Mohamad, Mohd Saberi Deris, Safaai Napis, Suhaimi Kasim, Shahreen |
| author_facet | Nasrudin, Nurul Athirah Chan, Weng Howe Mohamad, Mohd Saberi Deris, Safaai Napis, Suhaimi Kasim, Shahreen |
| author_sort | Nasrudin, Nurul Athirah |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Genomic knowledge has become a popular research field in bioinformatics biological process that providing further
biological process information. Many methods have been done to address the issues of high data throughput due to increased use of microarray technology. However, it is still not able to determine the appropriate diseases accurately. This is because of existing noninformative genes that could be included in the analysis of context-specific data like cancer gene expression data, which affect the classification performance. This study proposed a pathway-based analysis for gene classification. Pathway-based analysis enables handling microarray data in order to improve biological interpretation of the analysis outcome. Secondly, Support Vector Machine with Least Absolute Shrinkage and Selection Operator algorithm (SVM-LASSO) is proposed, which to find informative genes for each pathway to ensure efficient gene selection and classification in every pathway. Experiments are done using lung cancer dataset and breast cancer dataset that widely used in cancer classification area. A stratified 10-fold cross validation is implemented to evaluate the performance of the proposed method in terms of accuracy, specificity, and sensitivity. Moreover, biological validation has been done on the selected genes based on biological literature and biological databases. Next, the results from the proposed methods are compared with the previous study throughout all the data sets in terms of performance. As a conclusion, this research finding can contribute in biology area especially in cancer classification area. |
| first_indexed | 2025-11-15T11:13:11Z |
| format | Article |
| id | upm-62655 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T11:13:11Z |
| publishDate | 2017 |
| publisher | INSIGHT - Indonesian Society for Knowledge and Human Development |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-626552020-12-02T22:29:14Z http://psasir.upm.edu.my/id/eprint/62655/ Pathway-based analysis with Support Vector Machine (SVM-LASSO) for gene selection and classification Nasrudin, Nurul Athirah Chan, Weng Howe Mohamad, Mohd Saberi Deris, Safaai Napis, Suhaimi Kasim, Shahreen Genomic knowledge has become a popular research field in bioinformatics biological process that providing further biological process information. Many methods have been done to address the issues of high data throughput due to increased use of microarray technology. However, it is still not able to determine the appropriate diseases accurately. This is because of existing noninformative genes that could be included in the analysis of context-specific data like cancer gene expression data, which affect the classification performance. This study proposed a pathway-based analysis for gene classification. Pathway-based analysis enables handling microarray data in order to improve biological interpretation of the analysis outcome. Secondly, Support Vector Machine with Least Absolute Shrinkage and Selection Operator algorithm (SVM-LASSO) is proposed, which to find informative genes for each pathway to ensure efficient gene selection and classification in every pathway. Experiments are done using lung cancer dataset and breast cancer dataset that widely used in cancer classification area. A stratified 10-fold cross validation is implemented to evaluate the performance of the proposed method in terms of accuracy, specificity, and sensitivity. Moreover, biological validation has been done on the selected genes based on biological literature and biological databases. Next, the results from the proposed methods are compared with the previous study throughout all the data sets in terms of performance. As a conclusion, this research finding can contribute in biology area especially in cancer classification area. INSIGHT - Indonesian Society for Knowledge and Human Development 2017 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/62655/1/GENOMIC.pdf Nasrudin, Nurul Athirah and Chan, Weng Howe and Mohamad, Mohd Saberi and Deris, Safaai and Napis, Suhaimi and Kasim, Shahreen (2017) Pathway-based analysis with Support Vector Machine (SVM-LASSO) for gene selection and classification. International Journal on Advanced Science, Engineering and Information Technology, 7 (4-2). 1609 - 1614. ISSN 2088-5334; ESSN: 2460-6952 http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/3397 10.18517/ijaseit.7.4-2.3397 |
| spellingShingle | Nasrudin, Nurul Athirah Chan, Weng Howe Mohamad, Mohd Saberi Deris, Safaai Napis, Suhaimi Kasim, Shahreen Pathway-based analysis with Support Vector Machine (SVM-LASSO) for gene selection and classification |
| title | Pathway-based analysis with Support Vector Machine (SVM-LASSO) for gene selection and classification |
| title_full | Pathway-based analysis with Support Vector Machine (SVM-LASSO) for gene selection and classification |
| title_fullStr | Pathway-based analysis with Support Vector Machine (SVM-LASSO) for gene selection and classification |
| title_full_unstemmed | Pathway-based analysis with Support Vector Machine (SVM-LASSO) for gene selection and classification |
| title_short | Pathway-based analysis with Support Vector Machine (SVM-LASSO) for gene selection and classification |
| title_sort | pathway-based analysis with support vector machine (svm-lasso) for gene selection and classification |
| url | http://psasir.upm.edu.my/id/eprint/62655/ http://psasir.upm.edu.my/id/eprint/62655/ http://psasir.upm.edu.my/id/eprint/62655/ http://psasir.upm.edu.my/id/eprint/62655/1/GENOMIC.pdf |