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...
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| Format: | Article |
| Language: | English |
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Indonesian Society for Knowledge and Human Development
2017
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| Online Access: | http://umpir.ump.edu.my/id/eprint/29818/ http://umpir.ump.edu.my/id/eprint/29818/1/Pathway-based%20analysis%20with%20support%20vector%20machine.pdf |
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| author | Nurul Athirah, Nasrudin Chan, Weng Howe Mohd Saberi, Mohamad Safaai, Deris Suhaimi, Napis Shahreen, Kasim |
| author_facet | Nurul Athirah, Nasrudin Chan, Weng Howe Mohd Saberi, Mohamad Safaai, Deris Suhaimi, Napis Shahreen, Kasim |
| author_sort | Nurul Athirah, Nasrudin |
| building | UMP 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 non-informative 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 enable handling microarray data in order to improved 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 implement to evaluate the performance of the proposed method in terms of accuracy, specificity and sensitivity. Moreover, biological validation have been done on the selected genes based on biological literatures 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 conclusion, this research finding can contribute in biology area especially in cancer classification area. |
| first_indexed | 2025-11-15T02:56:04Z |
| format | Article |
| id | ump-29818 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T02:56:04Z |
| publishDate | 2017 |
| publisher | Indonesian Society for Knowledge and Human Development |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-298182020-11-13T02:16:11Z http://umpir.ump.edu.my/id/eprint/29818/ Pathway-based analysis with support vector machine (SVM-LASSO) for gene selection and classification Nurul Athirah, Nasrudin Chan, Weng Howe Mohd Saberi, Mohamad Safaai, Deris Suhaimi, Napis Shahreen, Kasim Q Science (General) TA Engineering (General). Civil engineering (General) 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 non-informative 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 enable handling microarray data in order to improved 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 implement to evaluate the performance of the proposed method in terms of accuracy, specificity and sensitivity. Moreover, biological validation have been done on the selected genes based on biological literatures 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 conclusion, this research finding can contribute in biology area especially in cancer classification area. Indonesian Society for Knowledge and Human Development 2017 Article PeerReviewed pdf en cc_by_sa_4 http://umpir.ump.edu.my/id/eprint/29818/1/Pathway-based%20analysis%20with%20support%20vector%20machine.pdf Nurul Athirah, Nasrudin and Chan, Weng Howe and Mohd Saberi, Mohamad and Safaai, Deris and Suhaimi, Napis and Shahreen, Kasim (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 Special). pp. 1609-1614. ISSN 2088-5334. (Published) https://doi.org/10.18517/ijaseit.7.4-2.3397 https://doi.org/10.18517/ijaseit.7.4-2.3397 |
| spellingShingle | Q Science (General) TA Engineering (General). Civil engineering (General) Nurul Athirah, Nasrudin Chan, Weng Howe Mohd Saberi, Mohamad Safaai, Deris Suhaimi, Napis Shahreen, Kasim 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 |
| topic | Q Science (General) TA Engineering (General). Civil engineering (General) |
| url | http://umpir.ump.edu.my/id/eprint/29818/ http://umpir.ump.edu.my/id/eprint/29818/ http://umpir.ump.edu.my/id/eprint/29818/ http://umpir.ump.edu.my/id/eprint/29818/1/Pathway-based%20analysis%20with%20support%20vector%20machine.pdf |