Evidence map›Paper›PMID 28947982›Full record

ArticleOncotarget2017

BRWLDA: bi-random walks for predicting lncRNA-disease associations.

Guoxian Yu, Guangyuan Fu, Chang Lu, Yazhou Ren, Jun Wang

Abstract read
In one paragraph

Article in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing 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

26 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. LDAEXC: LncRNA-Disease Associations Prediction with Deep Autoencoder and XGBoost Classifier.Interdisciplinary sciences, computational life sciences · 2023
    Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. Identifying Cancer-Related lncRNAs Based on a Convolutional Neural Network.Frontiers in cell and developmental biology · 2020
    Article
  16. Article
  17. Review
  18. Article
  19. LLCLPLDA: a novel model for predicting lncRNA-disease associations.Molecular genetics and genomics : MGG · 2019
    Article
  20. 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.

Guoxian YuCollege of Computer and Information Sciences, Southwest University, Chongqing, China.
Guangyuan FuCollege of Computer and Information Sciences, Southwest University, Chongqing, China.
Chang LuCollege of Computer and Information Sciences, Southwest University, Chongqing, China.
Yazhou RenBig Data Research Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China.
Jun WangCollege of Computer and Information Sciences, Southwest University, Chongqing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Increasing efforts have been done to figure out the association between lncRNAs and complex diseases. Many computational models construct various lncRNA similarity networks, disease similarity networks, along with known lncRNA-disease associations to infer novel associations. However, most of them neglect the structural difference between lncRNAs network and diseases network, hierarchical relationships between diseases and pattern of newly discovered associations. In this study, we developed a model that performs

Indexed as

bi-random walkbi-relational networkdiseaseslncRNA-disease associationslncRNAs

Identifiers

PMID28947982
PMCPMC5601150

What OpenQuestion holds

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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.