Evidence map›Paper›PMID 27228152›Full record

ArticlePloS one2016

LncRNApred: Classification of Long Non-Coding RNAs and Protein-Coding Transcripts by the Ensemble Algorithm with a New Hybrid Feature.

Cong Pian, Guangle Zhang, Zhi Chen, Yuanyuan Chen, Jin Zhang, Tao Yang, Liangyun Zhang

Abstract read
In one paragraph

Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing 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

23 citing papers in PubMed.

  1. Article
  2. DNABERT-based explainable lncRNA identification in plant genome assemblies.Computational and structural biotechnology journal · 2023
    Article
  3. Review
  4. Review
  5. The role of micropeptides in biology.Cellular and molecular life sciences : CMLS · 2021
    Review
  6. Article
  7. AI applications in functional genomics.Computational and structural biotechnology journal · 2021
    Review
  8. Article
  9. Article
  10. Article
  11. The computational approaches of lncRNA identification based on coding potential:Computational and structural biotechnology journal · 2020
    Review
  12. Article
  13. Article
  14. Genomic data mining for functional annotation of human long noncoding RNAs.Journal of Zhejiang University. Science. B · 2019
    Review
  15. Article
  16. Article
  17. Review
  18. Non-coding RNAs Shaping Muscle.Frontiers in cell and developmental biology · 2019
    Review
  19. Article
  20. 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

7 authors.

Cong PianDepartment of Mathematics, College of Science, Nanjing Agricultural University, Nanjing, Jiangsu, People's Republic of China.
Guangle ZhangDepartment of Mathematics, College of Science, Nanjing Agricultural University, Nanjing, Jiangsu, People's Republic of China.
Zhi ChenDepartment of Mathematics, College of Science, Nanjing Agricultural University, Nanjing, Jiangsu, People's Republic of China.
Yuanyuan ChenDepartment of Mathematics, College of Science, Nanjing Agricultural University, Nanjing, Jiangsu, People's Republic of China.
Jin ZhangDepartment of Mathematics, College of Science, Nanjing Agricultural University, Nanjing, Jiangsu, People's Republic of China.
Tao YangDepartment of Mathematics, College of Science, Nanjing Agricultural University, Nanjing, Jiangsu, People's Republic of China.
Liangyun ZhangDepartment of Mathematics, College of Science, Nanjing Agricultural University, Nanjing, Jiangsu, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As a novel class of noncoding RNAs, long noncoding RNAs (lncRNAs) have been verified to be associated with various diseases. As large scale transcripts are generated every year, it is significant to accurately and quickly identify lncRNAs from thousands of assembled transcripts. To accurately discover new lncRNAs, we develop a classification tool of random forest (RF) named LncRNApred based on a new hybrid feature. This hybrid feature set includes three new proposed features, which are MaxORF, RMaxORF and SNR. LncRNApred is effective for classifying lncRNAs and protein coding transcripts accurately and quickly. Moreover,our RF model only requests the training using data on human coding and non-coding transcripts. Other species can also be predicted by using LncRNApred. The result shows that our method is more effective compared with the Coding Potential Calculate (CPC). The web server of LncRNApred is available for free at http://mm20132014.wicp.net:57203/LncRNApred/home.jsp.

Indexed as

AlgorithmsRNA, Long NoncodingRNA, MessengerSoftwareSequence Analysis, RNARNA, Long NoncodingRNA, Messenger

Identifiers

PMID27228152
PMCPMC4882039

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.