Evidence map›Paper›PMID 31090273›Full record

ReviewJournal of Zhejiang University. Science. B2019

Genomic data mining for functional annotation of human long noncoding RNAs.

Brian L Gudenas, Jun Wang, Shu-Zhen Kuang, An-Qi Wei, Steven B Cogill, Liang-Jiang Wang

Abstract readReview
In one paragraph

Review in Journal of Zhejiang University. Science. B, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Different Data Mining Approaches Based Medical Text Data.Journal of healthcare engineering · 2021
    Review
  7. Article
  8. Deep analysis of RNA NNAR genomics and bioinformatics · 2020
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Role of LINC00152 in non-small cell lung cancer.Journal of Zhejiang University. Science. B
    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

6 authors.

Brian L GudenasDepartment of Genetics and Biochemistry, Clemson University, Clemson, South Carolina 29634, USA.
Jun WangDepartment of Genetics and Biochemistry, Clemson University, Clemson, South Carolina 29634, USA.
Shu-Zhen KuangDepartment of Genetics and Biochemistry, Clemson University, Clemson, South Carolina 29634, USA.
An-Qi WeiDepartment of Genetics and Biochemistry, Clemson University, Clemson, South Carolina 29634, USA.
Steven B CogillDepartment of Genetics and Biochemistry, Clemson University, Clemson, South Carolina 29634, USA.
Liang-Jiang WangDepartment of Genetics and Biochemistry, Clemson University, Clemson, South Carolina 29634, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Life may have begun in an RNA world, which is supported by increasing evidence of the vital role that RNAs perform in biological systems. In the human genome, most genes actually do not encode proteins; they are noncoding RNA genes. The largest class of noncoding genes is known as long noncoding RNAs (lncRNAs), which are transcripts greater in length than 200 nucleotides, but with no protein-coding capacity. While some lncRNAs have been demonstrated to be key regulators of gene expression and 3D genome organization, most lncRNAs are still uncharacterized. We thus propose several data mining and machine learning approaches for the functional annotation of human lncRNAs by leveraging the vast amount of data from genetic and genomic studies. Recent results from our studies and those of other groups indicate that genomic data mining can give insights into lncRNA functions and provide valuable information for experimental studies of candidate lncRNAs associated with human disease.

Indexed as

Data MiningGenomicsAutism Spectrum DisorderHumansMachine LearningRNA, Long NoncodingSupport Vector MachineRNA, Long NoncodingLong noncoding RNA; Functional annotation; Genomic data mining; Machine learning

Identifiers

PMID31090273
PMCPMC6568220

What OpenQuestion holds

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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.