Evidence map›Paper›PMID 36351980›Full record

ArticleScientific reports2022

LncDC: a machine learning-based tool for long non-coding RNA detection from RNA-Seq data.

Minghua Li, Chun Liang

Abstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing 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

27 citing papers in PubMed.

  1. Review
  2. Copy number evolution of conserved noncoding RNA gene families across mammals.Mammalian genome : official journal of the International Mammalian Genome Society · 2026
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  13. Regulatory Landscapes of Non-Coding RNAs During Drought Stress in Plants.International journal of molecular sciences · 2025
    Review
  14. Article
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  19. Review
  20. Computational Resources for lncRNA Functions and Targetome.Methods in molecular biology (Clifton, N.J.) · 2025
    Review
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

2 authors.

Minghua LiDepartment of Biology, Miami University, Oxford, OH, 45056, USA. lim74@miamioh.edu.
Chun LiangDepartment of Biology, Miami University, Oxford, OH, 45056, USA. liangc@miamioh.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Long non-coding RNAs (lncRNAs) play an essential role in diverse biological processes and disease development. Accurate classification of lncRNAs and mRNAs is important for the identification of tissue- or disease-specific lncRNAs. Here, we present our tool LncDC (Long non-coding RNA detection) that is able to accurately predict lncRNAs with an XGBoost model using features extracted from RNA sequences, secondary structures, and translated proteins. Benchmarking experiments showed that LncDC consistently outperformed six state-of-the-art tools in distinguishing lncRNAs from mRNAs. Notably, the use of sequence and secondary structure (SASS) k-mer score features and flexible ORF features improved the classification capability of LncDC. We anticipate that LncDC will definitely promote the discovery of more and novel disease-specific lncRNAs. LncDC is implemented in Python and freely available at https://github.com/lim74/LncDC .

Indexed as

RNA, Long NoncodingMachine LearningProteinsRNA, MessengerRNA-SeqProteinsRNA, Long NoncodingRNA, Messenger

Identifiers

PMID36351980
PMCPMC9646749

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.