Evidence map›Paper›PMID 39758831›Full record

ArticleBioinformatics advances2025

LncLSTA: a versatile predictor unveiling subcellular localization of lncRNAs through long-short term attention.

Kai Wang, Yueming Hu, Sida Li, Ming Chen, Zhong Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bioinformatics advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
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

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

2 citing papers in PubMed.

  1. New Sights into Bioinformatics of Gene Regulations and Structure.International journal of molecular sciences · 2025
    Article
  2. Article
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

5 authors.

Kai WangSchool of Information Engineering, Huzhou University, Huzhou, Zhejiang 313000, China.
Yueming HuCollege of Life Sciences, Zhejiang University, Hangzhou, Zhejiang 310003, China.ORCID https://orcid.org/0000-0003-2174-5987
Sida LiCollege of Life Sciences, Zhejiang University, Hangzhou, Zhejiang 310003, China.
Ming ChenCollege of Life Sciences, Zhejiang University, Hangzhou, Zhejiang 310003, China.ORCID https://orcid.org/0000-0002-9677-1699
Zhong LiSchool of Information Engineering, Huzhou University, Huzhou, Zhejiang 313000, China.ORCID https://orcid.org/0000-0002-1767-1519

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Motivation: Much evidence suggests that the subcellular localization of long-stranded noncoding RNAs (LncRNAs) provides key insights for the study of their biological function. Results: This study proposes a novel deep learning framework, LncLSTA, designed for predicting the subcellular localization of LncRNAs. It firstly exploits LncRNA sequence, electron-ion interaction pseudopotentials, and nucleotide chemical property as feature inputs. Departing from conventional Availability and implementation: The source code is publicly available at https://bis.zju.edu.cn/LncLSTA.

Identifiers

PMID39758831
PMCPMC11700581

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Registered trials

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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.