Survival Analysis by Penalized Regression and Matrix Factorization
Because every disease has its unique survival pattern, it is necessary to find a suitable model to simulate followups. DNA microarray is a useful technique to detect thousands of gene expressions at one time and is usually employed to classify different types of cancer. We propose combination method...
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2013/632030 |
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| author | Yeuntyng Lai Morihiro Hayashida Tatsuya Akutsu |
| author_facet | Yeuntyng Lai Morihiro Hayashida Tatsuya Akutsu |
| author_sort | Yeuntyng Lai |
| collection | DOAJ |
| description | Because every disease has its unique survival pattern, it is necessary
to find a suitable model to simulate followups. DNA microarray is a useful technique to detect thousands of gene expressions at one time and is usually employed to classify different types of cancer. We propose combination methods of penalized regression models and nonnegative matrix
factorization (NMF) for predicting survival. We tried L1- (lasso), L2- (ridge), and L1-L2 combined (elastic net) penalized regression for diffuse large B-cell lymphoma (DLBCL) patients'
microarray data and found that L1-L2 combined method predicts survival best with the smallest logrank P value. Furthermore, 80% of selected genes have been reported to correlate with carcinogenesis or lymphoma. Through NMF we found that DLBCL patients can be divided into 4 groups clearly, and it implies that DLBCL may have 4 subtypes which have a little different survival patterns. Next we excluded some patients who were indicated hard to classify in NMF and executed three penalized regression models again. We found that the performance of survival prediction has been improved with lower logrank P values. Therefore, we conclude that after preselection of patients by NMF, penalized regression models can predict DLBCL patients' survival successfully. |
| format | Article |
| id | doaj-art-fbd00d2e4e24490eb1db831767084fee |
| institution | Kabale University |
| issn | 1537-744X |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-fbd00d2e4e24490eb1db831767084fee2025-02-03T01:20:41ZengWileyThe Scientific World Journal1537-744X2013-01-01201310.1155/2013/632030632030Survival Analysis by Penalized Regression and Matrix FactorizationYeuntyng Lai0Morihiro Hayashida1Tatsuya Akutsu2Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, JapanBioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, JapanBioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, JapanBecause every disease has its unique survival pattern, it is necessary to find a suitable model to simulate followups. DNA microarray is a useful technique to detect thousands of gene expressions at one time and is usually employed to classify different types of cancer. We propose combination methods of penalized regression models and nonnegative matrix factorization (NMF) for predicting survival. We tried L1- (lasso), L2- (ridge), and L1-L2 combined (elastic net) penalized regression for diffuse large B-cell lymphoma (DLBCL) patients' microarray data and found that L1-L2 combined method predicts survival best with the smallest logrank P value. Furthermore, 80% of selected genes have been reported to correlate with carcinogenesis or lymphoma. Through NMF we found that DLBCL patients can be divided into 4 groups clearly, and it implies that DLBCL may have 4 subtypes which have a little different survival patterns. Next we excluded some patients who were indicated hard to classify in NMF and executed three penalized regression models again. We found that the performance of survival prediction has been improved with lower logrank P values. Therefore, we conclude that after preselection of patients by NMF, penalized regression models can predict DLBCL patients' survival successfully.http://dx.doi.org/10.1155/2013/632030 |
| spellingShingle | Yeuntyng Lai Morihiro Hayashida Tatsuya Akutsu Survival Analysis by Penalized Regression and Matrix Factorization The Scientific World Journal |
| title | Survival Analysis by Penalized Regression and Matrix Factorization |
| title_full | Survival Analysis by Penalized Regression and Matrix Factorization |
| title_fullStr | Survival Analysis by Penalized Regression and Matrix Factorization |
| title_full_unstemmed | Survival Analysis by Penalized Regression and Matrix Factorization |
| title_short | Survival Analysis by Penalized Regression and Matrix Factorization |
| title_sort | survival analysis by penalized regression and matrix factorization |
| url | http://dx.doi.org/10.1155/2013/632030 |
| work_keys_str_mv | AT yeuntynglai survivalanalysisbypenalizedregressionandmatrixfactorization AT morihirohayashida survivalanalysisbypenalizedregressionandmatrixfactorization AT tatsuyaakutsu survivalanalysisbypenalizedregressionandmatrixfactorization |