lncRNA localization and feature interpretability analysis

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.

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Publication Details

Journal
Molecular Therapy — Nucleic Acids
Published
2024-12-13
DOI
https://doi.org/10.1016/j.omtn.2024.102425
Citations
2
Primary Topic
Cancer-related molecular mechanisms research
Type
article
Field-Weighted Citation Impact
0.57

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article

lncRNA localization and feature interpretability analysis

Quan Zou, Ying Ju, Fengming Ni, Jing Li
2 citations
Molecular Therapy — Nucleic Acids
Cancer-related molecular mechanisms research
0.57
article

lncRNA localization and feature interpretability analysis

Quan Zou, Ying Ju, Fengming Ni, Jing Li
article en
2 citations

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.

Molecular Therapy — Nucleic AcidsVol. 36(1)
University of Electronic Science and Technology of China (CN), Xiamen University (CN), Jilin University (CN), Quzhou University (CN), First Hospital of Jilin University (CN), University of Hong Kong (HK)
National Natural Science Foundation of China
Openalex Percentile: Top 30%
Cancer-related molecular mechanisms research
0.57
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lncRNA localization and feature interpretability analysis — Quan Zou, Ying Ju, et al. · Molecular Therapy — Nucleic Acids (2024) | TGRS Research Map | TGRS