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长链非编码RNA定位与特征可解释性分析。

lncRNA localization and feature interpretability analysis.

作者信息

Li Jing, Ju Ying, Zou Quan, Ni Fengming

机构信息

Department of Microbiology, University of Hong Kong, Hong Kong, China.

School of Biomedical Sciences, University of Hong Kong, Hong Kong, China.

出版信息

Mol Ther Nucleic Acids. 2024 Dec 12;36(1):102425. doi: 10.1016/j.omtn.2024.102425. eCollection 2025 Mar 11.

Abstract

Subcellular localization is crucial for understanding the functions and regulatory mechanisms of biomolecules. Long non-coding RNAs (lncRNAs) have diverse roles in cellular processes, and their localization within specific subcellular compartments provides insights into their biological functions and implications in health and disease. The nucleolus and nucleoplasm are key hubs for RNA metabolism and cellular regulation. We developed a model, LncDNN, for identifying the localization of lncRNAs in the nucleolus and nucleoplasm. LncDNN uses three different encoding schemes and employs Shapley Additive Explanations for feature analysis and selection. The results show that LncDNN is more accurate than other models. Additionally, an interpretable analysis of the features influencing the model was conducted. LncDNN is applicable for identifying the localization of lncRNA in the nucleolus and nucleoplasm, aiding in the understanding and in-depth study of related biological processes and functions.

摘要

亚细胞定位对于理解生物分子的功能和调控机制至关重要。长链非编码RNA(lncRNA)在细胞过程中具有多种作用,它们在特定亚细胞区室中的定位为深入了解其生物学功能以及在健康和疾病中的影响提供了线索。核仁和核质是RNA代谢和细胞调控的关键枢纽。我们开发了一种名为LncDNN的模型,用于识别lncRNA在核仁和核质中的定位。LncDNN使用三种不同的编码方案,并采用Shapley加性解释进行特征分析和选择。结果表明,LncDNN比其他模型更准确。此外,还对影响该模型的特征进行了可解释分析。LncDNN适用于识别lncRNA在核仁和核质中的定位,有助于理解和深入研究相关的生物学过程和功能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8aba/11803160/35720bf52484/fx1.jpg

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