Evidence map›Paper›PMID 33348366›Full record

ArticleBriefings in bioinformatics2021

Topological network measures for drug repositioning.

Apurva Badkas, Sébastien De Landtsheer, Thomas Sauter

Abstract read
In one paragraph

Article in Briefings in bioinformatics, 2021. 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. Review
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Inferring Drug Set and Identifying the Mechanism of Drugs for PC3.International journal of molecular sciences · 2024
    Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Guiding Drug Repositioning for Cancers Based on Drug Similarity Networks.International journal of molecular sciences · 2023
    Article
  16. Boolean model of the gene regulatory network ofFrontiers in microbiology · 2023
    Article
  17. Article
  18. Review
  19. 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

3 authors.

Apurva BadkasUniversity of Luxembourg.ORCID 0000-0002-4120-1963
Sébastien De LandtsheerUniversity of Luxembourg.
Thomas SauterUniversity of Luxembourg.ORCID 0000-0001-8225-2954

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Drug repositioning has received increased attention since the past decade as several blockbuster drugs have come out of repositioning. Computational approaches are significantly contributing to these efforts, of which, network-based methods play a key role. Various structural (topological) network measures have thereby contributed to uncovering unintuitive functional relationships and repositioning candidates in drug-disease and other networks. This review gives a broad overview of the topic, and offers perspectives on the application of topological measures for network analysis. It also discusses unexplored measures, and draws attention to a wider scope of application efforts, especially in drug repositioning.

Indexed as

Computational BiologyDrug RepositioningMachine Learningcomputational methodsdrug repositioningnetworkstopological network measurestopology

Identifiers

PMID33348366
PMCPMC8294518

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.