Evidence map›Paper›PMID 39926317›Full record

ArticleMolecular therapy. Nucleic acids2025

lncRNA localization and feature interpretability analysis.

Jing Li, Ying Ju, Quan Zou, Fengming Ni

Abstract read
In one paragraph

Article in Molecular therapy. Nucleic acids, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Jing LiDepartment of Microbiology, University of Hong Kong, Hong Kong, China.
Ying JuSchool of Informatics, Xiamen University, Xiamen, China.
Quan ZouYangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, Zhejiang, China.
Fengming NiDepartment of Gastroenterology, The First Hospital of Jilin University, Changchun, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

feature selectionlncRNAsmachine learningMT: BioinformaticsnucleolusnucleoplasmSHAP analysissubcellular localization

Identifiers

PMID39926317
PMCPMC11803160

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.