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Identification of Dihydrouridine RNA modification sites through stacking strategy
https://ipsj.ixsq.nii.ac.jp/records/234923
https://ipsj.ixsq.nii.ac.jp/records/234923a2b07f85-7566-45e4-aa05-77b27c2a1632
| 名前 / ファイル | ライセンス | アクション |
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IPSJ-MPS24148036.pdf (532.6 kB)
2026年6月13日からダウンロード可能です。
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Copyright (c) 2024 by the Information Processing Society of Japan
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| 非会員：¥660, IPSJ:学会員：¥330, MPS:会員：¥0, DLIB:会員：¥0 | ||
| Item type | SIG Technical Reports(1) | |||||||||
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| 公開日 | 2024-06-13 | |||||||||
| タイトル | ||||||||||
| タイトル | Identification of Dihydrouridine RNA modification sites through stacking strategy | |||||||||
| タイトル | ||||||||||
| 言語 | en | |||||||||
| タイトル | Identification of Dihydrouridine RNA modification sites through stacking strategy | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | バイオ情報学2 | |||||||||
| 資源タイプ | ||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_18gh | |||||||||
| 資源タイプ | technical report | |||||||||
| 著者所属 | ||||||||||
| Department of Bioscience and Bioinformatics, Kyushu Institute of Technology | ||||||||||
| 著者所属 | ||||||||||
| Department of Bioscience and Bioinformatics, Kyushu Institute of Technology | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Department of Bioscience and Bioinformatics, Kyushu Institute of Technology | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Department of Bioscience and Bioinformatics, Kyushu Institute of Technology | ||||||||||
| 著者名 |
Md., Harun-Or-Roshid
× Md., Harun-Or-Roshid
× Hiroyuki, Kurata
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| 著者名(英) |
Md., Harun-Or-Roshid
× Md., Harun-Or-Roshid
× Hiroyuki, Kurata
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| 論文抄録 | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | Dihydrouridine (DHU, D) is a prevalent post-transcriptional modification in tRNA, mRNA, and snoRNA, linked to disease and biological processes in eukaryotes. Identifying D sites is crucial but experimental methods are costly and slow. To address this, we developed a computational tool that enhances prediction performance by integrating 66 baseline models through ensemble learning, named Stack-DHUpred. These models combine six machine-learning classifiers with eleven feature encoding methods. The best-performing combinations were used to construct the final stacked model. In tests, Stack-DHUpred surpassed existing predictors on the independent dataset, proving the efficacy of our stacking approach in accelerating the discovery and understanding of D modifications in post-transcriptional regulation. | |||||||||
| 論文抄録(英) | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | Dihydrouridine (DHU, D) is a prevalent post-transcriptional modification in tRNA, mRNA, and snoRNA, linked to disease and biological processes in eukaryotes. Identifying D sites is crucial but experimental methods are costly and slow. To address this, we developed a computational tool that enhances prediction performance by integrating 66 baseline models through ensemble learning, named Stack-DHUpred. These models combine six machine-learning classifiers with eleven feature encoding methods. The best-performing combinations were used to construct the final stacked model. In tests, Stack-DHUpred surpassed existing predictors on the independent dataset, proving the efficacy of our stacking approach in accelerating the discovery and understanding of D modifications in post-transcriptional regulation. | |||||||||
| 書誌レコードID | ||||||||||
| 収録物識別子タイプ | NCID | |||||||||
| 収録物識別子 | AN10505667 | |||||||||
| 書誌情報 |
研究報告数理モデル化と問題解決（MPS） 巻 2024-MPS-148, 号 36, p. 1-2, 発行日 2024-06-13 |
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| ISSN | ||||||||||
| 収録物識別子タイプ | ISSN | |||||||||
| 収録物識別子 | 2188-8833 | |||||||||
| Notice | ||||||||||
| SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc. | ||||||||||
| 出版者 | ||||||||||
| 言語 | ja | |||||||||
| 出版者 | 情報処理学会 | |||||||||
