Evidence map›Paper›PMID 27340225›Full record

ReviewHuman molecular genetics2016

Pathway and network-based strategies to translate genetic discoveries into effective therapies.

Casey S Greene, Benjamin F Voight

Open access · bronzeAbstract readReview
In one paragraph

Review in Human molecular genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
0.9field-weighted citation impact, top 24% of its field
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

18 citing papers in PubMed, 46 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Review
  5. Informatics on Drug Repurposing for Breast Cancer.Drug design, development and therapy · 2023
    Review
  6. Review
  7. Review
  8. Review
  9. Review
  10. Review
  11. Review
  12. Network medicine framework for identifying drug-repurposing opportunities for COVID-19.Proceedings of the National Academy of Sciences of the United States of America · 2021
    Article
  13. Article
  14. Review
  15. Article
  16. Review
  17. Review
  18. 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

2 authors at 1 institution in 1 country.

Casey S GreeneDepartment of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine Institute for Translational Medicine and Therapeutics, Perelman School of Medicine csgreene@upenn.edu.
Benjamin F VoightDepartment of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine Institute for Translational Medicine and Therapeutics, Perelman School of Medicine Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19103 USA.
Translational Therapeutics (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

One way to design a drug is to attempt to phenocopy a genetic variant that is known to have the desired effect. In general, drugs that are supported by genetic associations progress further in the development pipeline. However, the number of associations that are candidates for development into drugs is limited because many associations are in non-coding regions or difficult to target genes. Approaches that overlay information from pathway databases or biological networks can expand the potential target list. In cases where the initial variant is not targetable or there is no variant with the desired effect, this may reveal new means to target a disease. In this review, we discuss recent examples in the domain of pathway and network-based drug repositioning from genetic associations. We highlight important caveats and challenges for the field, and we discuss opportunities for further development.

Identifiers

PMID27340225
PMCPMC5036870
OpenAlexW2464343370

What OpenQuestion holds

Textmetadata
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.