Identification of Dihydrouridine RNA modification sites through stacking strategy

• Item type: SIG Technical Reports(1)
• 公開日: 2024-06-13
• 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: eng
• キーワード / 主題Scheme: Other
• キーワード / 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
• 著者所属(英): Department of Bioscience and Bioinformatics, Kyushu Institute of Technology
• 著者所属(英): en
• 著者所属(英): Department of Bioscience and Bioinformatics, Kyushu Institute of Technology
• 著者名: Md., Harun-Or-Roshid; Hiroyuki, Kurata
• 著者名(英): Md., Harun-Or-Roshid; Hiroyuki, Kurata
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 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
• 論文抄録(英) / 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
• 書誌レコードID / 収録物識別子: AA12055912
• 書誌情報: 研究報告バイオ情報学（BIO）; 巻 2024-BIO-78, 号 36, p. 1-2, 発行日 2024-06-13
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 2188-8590
• 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