Novel hybrid DCNN–SVM model for classifying RNA-sequencing gene expression data
In recent years, cancer is one of the leading causes of death worldwide. Therefore, there are more and more studies that have been conducted to find effective solutions to diagnose and treat cancer. However, there are still many challenges in cancer treatment because possible causes of cancer are ge...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2019-10-01
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| Series: | Journal of Information and Telecommunication |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/24751839.2019.1660845 |
| _version_ | 1818849190137036800 |
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| author | Phuoc-Hai Huynh Van-Hoa Nguyen Thanh-Nghi Do |
| author_facet | Phuoc-Hai Huynh Van-Hoa Nguyen Thanh-Nghi Do |
| author_sort | Phuoc-Hai Huynh |
| collection | DOAJ |
| description | In recent years, cancer is one of the leading causes of death worldwide. Therefore, there are more and more studies that have been conducted to find effective solutions to diagnose and treat cancer. However, there are still many challenges in cancer treatment because possible causes of cancer are genetic disorders or epigenetic alterations in the cells. RNA sequencing is a powerful technique for gene expression profiling in model organisms and it is able to produce information for diagnosing cancer at the biomolecular level. Gene expression data are used to build a classification model which supports treatment of cancer. Nevertheless, its characteristic is very-high-dimensional data which lead to over-fitting issue of classifying model. In this paper, we propose a new gene expression classification model of support vector machines (SVM) using features extracted by deep convolutional neural network (DCNN). In our approach, the DCNN extracts latent features from gene expression data, then they are used in conjunction with SVM that efficiently classify RNA-Seq gene expression data. Numerical test results on RNA-Seq gene expression datasets from The Cancer Genome Atlas (TCGA) illustrate that our proposed algorithm is more accurate than state-of-the-art classifying models including DCNN, SVM and random forests. |
| first_indexed | 2024-12-19T06:29:18Z |
| format | Article |
| id | doaj.art-caf7bc9c4ab94b52866438a9574796c2 |
| institution | Directory Open Access Journal |
| issn | 2475-1839 2475-1847 |
| language | English |
| last_indexed | 2024-12-19T06:29:18Z |
| publishDate | 2019-10-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Information and Telecommunication |
| spelling | doaj.art-caf7bc9c4ab94b52866438a9574796c22022-12-21T20:32:27ZengTaylor & Francis GroupJournal of Information and Telecommunication2475-18392475-18472019-10-013453354710.1080/24751839.2019.16608451660845Novel hybrid DCNN–SVM model for classifying RNA-sequencing gene expression dataPhuoc-Hai Huynh0Van-Hoa Nguyen1Thanh-Nghi Do2An Giang UniversityAn Giang UniversityCan Tho UniversityIn recent years, cancer is one of the leading causes of death worldwide. Therefore, there are more and more studies that have been conducted to find effective solutions to diagnose and treat cancer. However, there are still many challenges in cancer treatment because possible causes of cancer are genetic disorders or epigenetic alterations in the cells. RNA sequencing is a powerful technique for gene expression profiling in model organisms and it is able to produce information for diagnosing cancer at the biomolecular level. Gene expression data are used to build a classification model which supports treatment of cancer. Nevertheless, its characteristic is very-high-dimensional data which lead to over-fitting issue of classifying model. In this paper, we propose a new gene expression classification model of support vector machines (SVM) using features extracted by deep convolutional neural network (DCNN). In our approach, the DCNN extracts latent features from gene expression data, then they are used in conjunction with SVM that efficiently classify RNA-Seq gene expression data. Numerical test results on RNA-Seq gene expression datasets from The Cancer Genome Atlas (TCGA) illustrate that our proposed algorithm is more accurate than state-of-the-art classifying models including DCNN, SVM and random forests.http://dx.doi.org/10.1080/24751839.2019.1660845deep convolutional neural networksupport vector machinesrna-sequencing gene expressionclassification |
| spellingShingle | Phuoc-Hai Huynh Van-Hoa Nguyen Thanh-Nghi Do Novel hybrid DCNN–SVM model for classifying RNA-sequencing gene expression data Journal of Information and Telecommunication deep convolutional neural network support vector machines rna-sequencing gene expression classification |
| title | Novel hybrid DCNN–SVM model for classifying RNA-sequencing gene expression data |
| title_full | Novel hybrid DCNN–SVM model for classifying RNA-sequencing gene expression data |
| title_fullStr | Novel hybrid DCNN–SVM model for classifying RNA-sequencing gene expression data |
| title_full_unstemmed | Novel hybrid DCNN–SVM model for classifying RNA-sequencing gene expression data |
| title_short | Novel hybrid DCNN–SVM model for classifying RNA-sequencing gene expression data |
| title_sort | novel hybrid dcnn svm model for classifying rna sequencing gene expression data |
| topic | deep convolutional neural network support vector machines rna-sequencing gene expression classification |
| url | http://dx.doi.org/10.1080/24751839.2019.1660845 |
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