Evidence map›Paper›PMID 35521545›Full record

ArticleComputational and structural biotechnology journal2022

Noncoding RNAs endogenously rule the cancerous regulatory realm while proteins govern the normal.

Anyou Wang

Abstract read
In one paragraph

Article in Computational and structural biotechnology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Review
  5. Article
  6. Distinctive functional regime of endogenous lncRNAs in dark regions of human genome.Computational and structural biotechnology journal · 2022
    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

1 author.

Anyou WangThe Institute for Integrative Genome Biology, University of California at Riverside, Riverside, CA 92521, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancers evolve from normal tissues and share an endogenous regulatory realm distinctive from that of normal human tissues. Unearthing such an endogenous realm faces challenges due to heterogeneous biology data. This study computes petabyte level data and reveals the endogenous regulatory networks of normal and cancers and then unearths the most important endogenous regulators for normal and cancerous realm. In normal, proteins dominate the entire realm and

Indexed as

Big dataCancerEndogenousFINETNoncoding RNAPseudogeneRegulatory networkSystems

Identifiers

PMID35521545
PMCPMC9062140

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.