Identification of D Modification Sites Using a Random Forest Model Based on Nucleotide Chemical Properties
Dihydrouridine (D) is an abundant post-transcriptional modification present in transfer RNA from eukaryotes, bacteria, and archaea. D has contributed to treatments for cancerous diseases. Therefore, the precise detection of D modification sites can enable further understanding of its functional role...
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| Format: | Article |
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MDPI AG
2022-03-01
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| Series: | International Journal of Molecular Sciences |
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| Online Access: | https://www.mdpi.com/1422-0067/23/6/3044 |
| _version_ | 1797470973537026048 |
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| author | Huan Zhu Chun-Yan Ao Yi-Jie Ding Hong-Xia Hao Liang Yu |
| author_facet | Huan Zhu Chun-Yan Ao Yi-Jie Ding Hong-Xia Hao Liang Yu |
| author_sort | Huan Zhu |
| collection | DOAJ |
| description | Dihydrouridine (D) is an abundant post-transcriptional modification present in transfer RNA from eukaryotes, bacteria, and archaea. D has contributed to treatments for cancerous diseases. Therefore, the precise detection of D modification sites can enable further understanding of its functional roles. Traditional experimental techniques to identify D are laborious and time-consuming. In addition, there are few computational tools for such analysis. In this study, we utilized eleven sequence-derived feature extraction methods and implemented five popular machine algorithms to identify an optimal model. During data preprocessing, data were partitioned for training and testing. Oversampling was also adopted to reduce the effect of the imbalance between positive and negative samples. The best-performing model was obtained through a combination of random forest and nucleotide chemical property modeling. The optimized model presented high sensitivity and specificity values of 0.9688 and 0.9706 in independent tests, respectively. Our proposed model surpassed published tools in independent tests. Furthermore, a series of validations across several aspects was conducted in order to demonstrate the robustness and reliability of our model. |
| first_indexed | 2024-03-09T19:42:59Z |
| format | Article |
| id | doaj.art-049d1eb96de041688742650d94292743 |
| institution | Directory Open Access Journal |
| issn | 1661-6596 1422-0067 |
| language | English |
| last_indexed | 2024-03-09T19:42:59Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Molecular Sciences |
| spelling | doaj.art-049d1eb96de041688742650d942927432023-11-24T01:31:06ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672022-03-01236304410.3390/ijms23063044Identification of D Modification Sites Using a Random Forest Model Based on Nucleotide Chemical PropertiesHuan Zhu0Chun-Yan Ao1Yi-Jie Ding2Hong-Xia Hao3Liang Yu4School of Computer Science and Technology, Xidian University, Xi’an 710071, ChinaSchool of Computer Science and Technology, Xidian University, Xi’an 710071, ChinaYangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, ChinaSchool of Computer Science and Technology, Xidian University, Xi’an 710071, ChinaSchool of Computer Science and Technology, Xidian University, Xi’an 710071, ChinaDihydrouridine (D) is an abundant post-transcriptional modification present in transfer RNA from eukaryotes, bacteria, and archaea. D has contributed to treatments for cancerous diseases. Therefore, the precise detection of D modification sites can enable further understanding of its functional roles. Traditional experimental techniques to identify D are laborious and time-consuming. In addition, there are few computational tools for such analysis. In this study, we utilized eleven sequence-derived feature extraction methods and implemented five popular machine algorithms to identify an optimal model. During data preprocessing, data were partitioned for training and testing. Oversampling was also adopted to reduce the effect of the imbalance between positive and negative samples. The best-performing model was obtained through a combination of random forest and nucleotide chemical property modeling. The optimized model presented high sensitivity and specificity values of 0.9688 and 0.9706 in independent tests, respectively. Our proposed model surpassed published tools in independent tests. Furthermore, a series of validations across several aspects was conducted in order to demonstrate the robustness and reliability of our model.https://www.mdpi.com/1422-0067/23/6/3044dihydrouridinerandom forestnucleotide chemical propertiespredictionoversample |
| spellingShingle | Huan Zhu Chun-Yan Ao Yi-Jie Ding Hong-Xia Hao Liang Yu Identification of D Modification Sites Using a Random Forest Model Based on Nucleotide Chemical Properties International Journal of Molecular Sciences dihydrouridine random forest nucleotide chemical properties prediction oversample |
| title | Identification of D Modification Sites Using a Random Forest Model Based on Nucleotide Chemical Properties |
| title_full | Identification of D Modification Sites Using a Random Forest Model Based on Nucleotide Chemical Properties |
| title_fullStr | Identification of D Modification Sites Using a Random Forest Model Based on Nucleotide Chemical Properties |
| title_full_unstemmed | Identification of D Modification Sites Using a Random Forest Model Based on Nucleotide Chemical Properties |
| title_short | Identification of D Modification Sites Using a Random Forest Model Based on Nucleotide Chemical Properties |
| title_sort | identification of d modification sites using a random forest model based on nucleotide chemical properties |
| topic | dihydrouridine random forest nucleotide chemical properties prediction oversample |
| url | https://www.mdpi.com/1422-0067/23/6/3044 |
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