Gene selection in cox regression model based on a new adaptive elastic net penalty
Regression analysis is great of interest in several studies, especially in medicine. The Cox regression model is one of the most important models of regression used in the medical field. It is the tool by which the dependent variable is modeled when the values of that variable are in the form of sur...
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| Format: | Article |
| Language: | Arabic |
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College of Computer Science and Mathematics, University of Mosul
2020-12-01
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| Series: | المجلة العراقية للعلوم الاحصائية |
| Subjects: | |
| Online Access: | https://stats.mosuljournals.com/article_167386_0a789d7fa435a1c9877b692bba6c9251.pdf |
| _version_ | 1818219153069178880 |
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| author | Oday Alskal Zakariya Algamal |
| author_facet | Oday Alskal Zakariya Algamal |
| author_sort | Oday Alskal |
| collection | DOAJ |
| description | Regression analysis is great of interest in several studies, especially in medicine. The Cox regression model is one of the most important models of regression used in the medical field. It is the tool by which the dependent variable is modeled when the values of that variable are in the form of survival time data. As in linear regression model, the Cox regression model may contain many explanatory variables, which negatively affects the accuracy of the model and its simplicity in interpreting the results. The common issues of high dimensional gene expression data for survival analysis are that many of genes may not be relevant to their diseases. Gene selection has been proved to be an effective way to improve the result of many methods. The Cox regression model is the most popular model in regression analysis for censored survival data. In this paper, a new adaptive elastic net penalty with Cox regression model is proposed, with the aim of identification relevant genes and provides high classification accuracy, by combining the Cox regression model with the weighted L1-norm. Experimental results show that the proposed method significantly outperforms two competitor methods in terms of the area under the curve and the number of the selected genes. |
| first_indexed | 2024-12-12T07:35:07Z |
| format | Article |
| id | doaj.art-6cd55a393a7f40f49ce4953f15947ce5 |
| institution | Directory Open Access Journal |
| issn | 1680-855X 2664-2956 |
| language | Arabic |
| last_indexed | 2024-12-12T07:35:07Z |
| publishDate | 2020-12-01 |
| publisher | College of Computer Science and Mathematics, University of Mosul |
| record_format | Article |
| series | المجلة العراقية للعلوم الاحصائية |
| spelling | doaj.art-6cd55a393a7f40f49ce4953f15947ce52022-12-22T00:32:56ZaraCollege of Computer Science and Mathematics, University of Mosulالمجلة العراقية للعلوم الاحصائية1680-855X2664-29562020-12-01172273610.33899/iqjoss.2020.167386167386Gene selection in cox regression model based on a new adaptive elastic net penaltyOday Alskal0Zakariya Algamal1Department of Statistics and Informatics, University of Mosul, Mosul, IraqDept. of Statistics and Informatics/ college of computer sciences and mathematics/ University of Mosul, Mosul, iraqRegression analysis is great of interest in several studies, especially in medicine. The Cox regression model is one of the most important models of regression used in the medical field. It is the tool by which the dependent variable is modeled when the values of that variable are in the form of survival time data. As in linear regression model, the Cox regression model may contain many explanatory variables, which negatively affects the accuracy of the model and its simplicity in interpreting the results. The common issues of high dimensional gene expression data for survival analysis are that many of genes may not be relevant to their diseases. Gene selection has been proved to be an effective way to improve the result of many methods. The Cox regression model is the most popular model in regression analysis for censored survival data. In this paper, a new adaptive elastic net penalty with Cox regression model is proposed, with the aim of identification relevant genes and provides high classification accuracy, by combining the Cox regression model with the weighted L1-norm. Experimental results show that the proposed method significantly outperforms two competitor methods in terms of the area under the curve and the number of the selected genes.https://stats.mosuljournals.com/article_167386_0a789d7fa435a1c9877b692bba6c9251.pdfcox regression modelpenalized methodelastic netgene selection |
| spellingShingle | Oday Alskal Zakariya Algamal Gene selection in cox regression model based on a new adaptive elastic net penalty المجلة العراقية للعلوم الاحصائية cox regression model penalized method elastic net gene selection |
| title | Gene selection in cox regression model based on a new adaptive elastic net penalty |
| title_full | Gene selection in cox regression model based on a new adaptive elastic net penalty |
| title_fullStr | Gene selection in cox regression model based on a new adaptive elastic net penalty |
| title_full_unstemmed | Gene selection in cox regression model based on a new adaptive elastic net penalty |
| title_short | Gene selection in cox regression model based on a new adaptive elastic net penalty |
| title_sort | gene selection in cox regression model based on a new adaptive elastic net penalty |
| topic | cox regression model penalized method elastic net gene selection |
| url | https://stats.mosuljournals.com/article_167386_0a789d7fa435a1c9877b692bba6c9251.pdf |
| work_keys_str_mv | AT odayalskal geneselectionincoxregressionmodelbasedonanewadaptiveelasticnetpenalty AT zakariyaalgamal geneselectionincoxregressionmodelbasedonanewadaptiveelasticnetpenalty |