PESM: predicting the essentiality of miRNAs based on gradient boosting machines and sequences
Abstract Background MicroRNAs (miRNAs) are a kind of small noncoding RNA molecules that are direct posttranscriptional regulations of mRNA targets. Studies have indicated that miRNAs play key roles in complex diseases by taking part in many biological processes, such as cell growth, cell death and s...
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| Format: | Article |
| Language: | English |
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BMC
2020-03-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12859-020-3426-9 |
| _version_ | 1818296902667468800 |
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| author | Cheng Yan Fang-Xiang Wu Jianxin Wang Guihua Duan |
| author_facet | Cheng Yan Fang-Xiang Wu Jianxin Wang Guihua Duan |
| author_sort | Cheng Yan |
| collection | DOAJ |
| description | Abstract Background MicroRNAs (miRNAs) are a kind of small noncoding RNA molecules that are direct posttranscriptional regulations of mRNA targets. Studies have indicated that miRNAs play key roles in complex diseases by taking part in many biological processes, such as cell growth, cell death and so on. Therefore, in order to improve the effectiveness of disease diagnosis and treatment, it is appealing to develop advanced computational methods for predicting the essentiality of miRNAs. Result In this study, we propose a method (PESM) to predict the miRNA essentiality based on gradient boosting machines and miRNA sequences. First, PESM extracts the sequence and structural features of miRNAs. Then it uses gradient boosting machines to predict the essentiality of miRNAs. We conduct the 5-fold cross-validation to assess the prediction performance of our method. The area under the receiver operating characteristic curve (AUC), F-measure and accuracy (ACC) are used as the metrics to evaluate the prediction performance. We also compare PESM with other three competing methods which include miES, Gaussian Naive Bayes and Support Vector Machine. Conclusion The results of experiments show that PESM achieves the better prediction performance (AUC: 0.9117, F-measure: 0.8572, ACC: 0.8516) than other three computing methods. In addition, the relative importance of all features also further shows that newly added features can be helpful to improve the prediction performance of methods. |
| first_indexed | 2024-12-13T04:10:55Z |
| format | Article |
| id | doaj.art-844374cdcdf140809df453470a38dd29 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-13T04:10:55Z |
| publishDate | 2020-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-844374cdcdf140809df453470a38dd292022-12-22T00:00:03ZengBMCBMC Bioinformatics1471-21052020-03-012111910.1186/s12859-020-3426-9PESM: predicting the essentiality of miRNAs based on gradient boosting machines and sequencesCheng Yan0Fang-Xiang Wu1Jianxin Wang2Guihua Duan3Hunan Provincial Key Lab on Bioinformtics, School of Computer Science and Engineering, Central South UniversityBiomedical Engineering and Department of Mechanical Engineering, University of SaskatchewanHunan Provincial Key Lab on Bioinformtics, School of Computer Science and Engineering, Central South UniversityHunan Provincial Key Lab on Bioinformtics, School of Computer Science and Engineering, Central South UniversityAbstract Background MicroRNAs (miRNAs) are a kind of small noncoding RNA molecules that are direct posttranscriptional regulations of mRNA targets. Studies have indicated that miRNAs play key roles in complex diseases by taking part in many biological processes, such as cell growth, cell death and so on. Therefore, in order to improve the effectiveness of disease diagnosis and treatment, it is appealing to develop advanced computational methods for predicting the essentiality of miRNAs. Result In this study, we propose a method (PESM) to predict the miRNA essentiality based on gradient boosting machines and miRNA sequences. First, PESM extracts the sequence and structural features of miRNAs. Then it uses gradient boosting machines to predict the essentiality of miRNAs. We conduct the 5-fold cross-validation to assess the prediction performance of our method. The area under the receiver operating characteristic curve (AUC), F-measure and accuracy (ACC) are used as the metrics to evaluate the prediction performance. We also compare PESM with other three competing methods which include miES, Gaussian Naive Bayes and Support Vector Machine. Conclusion The results of experiments show that PESM achieves the better prediction performance (AUC: 0.9117, F-measure: 0.8572, ACC: 0.8516) than other three computing methods. In addition, the relative importance of all features also further shows that newly added features can be helpful to improve the prediction performance of methods.http://link.springer.com/article/10.1186/s12859-020-3426-9MiRNAEssentialityGradient boosting machines |
| spellingShingle | Cheng Yan Fang-Xiang Wu Jianxin Wang Guihua Duan PESM: predicting the essentiality of miRNAs based on gradient boosting machines and sequences BMC Bioinformatics MiRNA Essentiality Gradient boosting machines |
| title | PESM: predicting the essentiality of miRNAs based on gradient boosting machines and sequences |
| title_full | PESM: predicting the essentiality of miRNAs based on gradient boosting machines and sequences |
| title_fullStr | PESM: predicting the essentiality of miRNAs based on gradient boosting machines and sequences |
| title_full_unstemmed | PESM: predicting the essentiality of miRNAs based on gradient boosting machines and sequences |
| title_short | PESM: predicting the essentiality of miRNAs based on gradient boosting machines and sequences |
| title_sort | pesm predicting the essentiality of mirnas based on gradient boosting machines and sequences |
| topic | MiRNA Essentiality Gradient boosting machines |
| url | http://link.springer.com/article/10.1186/s12859-020-3426-9 |
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