Evidence map›Paper›PMID 41675960›Full record

ArticleQuantitative biology (Beijing, China)2025

Loc4Lnc: Accurate prediction of long noncoding RNA subcellular localization via enhanced RNA sequence representation.

Yujia Cheng, Xiaoyong Pan, Yang Yang

Abstract read
In one paragraph

Article in Quantitative biology (Beijing, China), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Yujia ChengDepartment of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China.ORCID https://orcid.org/0009-0003-7216-6309
Xiaoyong PanInstitute of Image Processing and Pattern Recognition and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China.
Yang YangDepartment of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Long noncoding RNAs (lncRNAs) are crucial in gene regulation, chromatin architecture, and cellular differentiation, playing significant roles in various diseases and serving as potential biomarkers and therapeutic targets. Understanding their precise subcellular localization is essential for elucidating their functions in biological pathways. Current methods for predicting lncRNA subcellular localization face challenges in capturing long-range interactions within sequences. Deep learning models often struggle with feature extraction that adequately represents these distant dependencies, leading to limited predictive accuracy. We develop Loc4Lnc, a deep learning framework for predicting lncRNA subcellular localization. The model integrates convolutional layers and transformer blocks to effectively capture both local sequence motifs and long-range dependencies within RNA sequences, followed by classification using TextCNN. Using the RNALocate v2.0 database, we constructed a benchmark dataset covering five subcellular locations (cytoplasm, nucleus, cytosol, chromatin, and exosome). The performance of the model is evaluated against existing feature extraction methods and existing predictors. Results of the Loc4Lnc study demonstrate significant improvements in predicting lncRNA subcellular localization. The model achieved a prediction accuracy of 0.636 on an independent test set, outperforming existing methodologies. Comparative evaluations showed that it consistently surpassed traditional feature extraction methods and state-of-the-art predictors, highlighting its robustness and effectiveness in accurately classifying lncRNAs across five distinct subcellular locations. Loc4Lnc effectively captures long-range interactions and optimizes information flow between distal elements, providing an effective predictive tool for the subcellular localization of lncRNAs and laying the foundation for future research on the regulation of gene expression and cellular functions by lncRNAs.

Indexed as

lncRNAlong sequence analysissubcellular localization

Identifiers

PMID41675960
PMCPMC12806047

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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.