6mA-StackingCV: an improved stacking ensemble model for predicting DNA N6-methyladenine site
Abstract DNA N6-adenine methylation (N6-methyladenine, 6mA) plays a key regulating role in the cellular processes. Precisely recognizing 6mA sites is of importance to further explore its biological functions. Although there are many developed computational methods for 6mA site prediction over the pa...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2023-11-01
|
| Series: | BioData Mining |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13040-023-00348-8 |
| _version_ | 1827604199258456064 |
|---|---|
| author | Guohua Huang Xiaohong Huang Wei Luo |
| author_facet | Guohua Huang Xiaohong Huang Wei Luo |
| author_sort | Guohua Huang |
| collection | DOAJ |
| description | Abstract DNA N6-adenine methylation (N6-methyladenine, 6mA) plays a key regulating role in the cellular processes. Precisely recognizing 6mA sites is of importance to further explore its biological functions. Although there are many developed computational methods for 6mA site prediction over the past decades, there is a large root left to improve. We presented a cross validation-based stacking ensemble model for 6mA site prediction, called 6mA-StackingCV. The 6mA-StackingCV is a type of meta-learning algorithm, which uses output of cross validation as input to the final classifier. The 6mA-StackingCV reached the state of the art performances in the Rosaceae independent test. Extensive tests demonstrated the stability and the flexibility of the 6mA-StackingCV. We implemented the 6mA-StackingCV as a user-friendly web application, which allows one to restrictively choose representations or learning algorithms. This application is freely available at http://www.biolscience.cn/6mA-stackingCV/ . The source code and experimental data is available at https://github.com/Xiaohong-source/6mA-stackingCV . |
| first_indexed | 2024-03-09T05:55:41Z |
| format | Article |
| id | doaj.art-c900565549ad4182a5ed722c3bb5d4af |
| institution | Directory Open Access Journal |
| issn | 1756-0381 |
| language | English |
| last_indexed | 2024-03-09T05:55:41Z |
| publishDate | 2023-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BioData Mining |
| spelling | doaj.art-c900565549ad4182a5ed722c3bb5d4af2023-12-03T12:14:25ZengBMCBioData Mining1756-03812023-11-0116111510.1186/s13040-023-00348-86mA-StackingCV: an improved stacking ensemble model for predicting DNA N6-methyladenine siteGuohua Huang0Xiaohong Huang1Wei Luo2School of Information Technology and Administration, Hunan University of Finance and EconomicsCollege of Information Science and Engineering, Shaoyang UniversityCollege of Information Science and Engineering, Shaoyang UniversityAbstract DNA N6-adenine methylation (N6-methyladenine, 6mA) plays a key regulating role in the cellular processes. Precisely recognizing 6mA sites is of importance to further explore its biological functions. Although there are many developed computational methods for 6mA site prediction over the past decades, there is a large root left to improve. We presented a cross validation-based stacking ensemble model for 6mA site prediction, called 6mA-StackingCV. The 6mA-StackingCV is a type of meta-learning algorithm, which uses output of cross validation as input to the final classifier. The 6mA-StackingCV reached the state of the art performances in the Rosaceae independent test. Extensive tests demonstrated the stability and the flexibility of the 6mA-StackingCV. We implemented the 6mA-StackingCV as a user-friendly web application, which allows one to restrictively choose representations or learning algorithms. This application is freely available at http://www.biolscience.cn/6mA-stackingCV/ . The source code and experimental data is available at https://github.com/Xiaohong-source/6mA-stackingCV .https://doi.org/10.1186/s13040-023-00348-8Cross validationMeta-learning6mADNA methylationEnsemble learning |
| spellingShingle | Guohua Huang Xiaohong Huang Wei Luo 6mA-StackingCV: an improved stacking ensemble model for predicting DNA N6-methyladenine site BioData Mining Cross validation Meta-learning 6mA DNA methylation Ensemble learning |
| title | 6mA-StackingCV: an improved stacking ensemble model for predicting DNA N6-methyladenine site |
| title_full | 6mA-StackingCV: an improved stacking ensemble model for predicting DNA N6-methyladenine site |
| title_fullStr | 6mA-StackingCV: an improved stacking ensemble model for predicting DNA N6-methyladenine site |
| title_full_unstemmed | 6mA-StackingCV: an improved stacking ensemble model for predicting DNA N6-methyladenine site |
| title_short | 6mA-StackingCV: an improved stacking ensemble model for predicting DNA N6-methyladenine site |
| title_sort | 6ma stackingcv an improved stacking ensemble model for predicting dna n6 methyladenine site |
| topic | Cross validation Meta-learning 6mA DNA methylation Ensemble learning |
| url | https://doi.org/10.1186/s13040-023-00348-8 |
| work_keys_str_mv | AT guohuahuang 6mastackingcvanimprovedstackingensemblemodelforpredictingdnan6methyladeninesite AT xiaohonghuang 6mastackingcvanimprovedstackingensemblemodelforpredictingdnan6methyladeninesite AT weiluo 6mastackingcvanimprovedstackingensemblemodelforpredictingdnan6methyladeninesite |