Evidence map›Paper›PMID 28487472›Full record

ReviewBioscience reports2017

Bioinformatics in translational drug discovery.

Sarah K Wooller, Graeme Benstead-Hume, Xiangrong Chen, Yusuf Ali, Frances M G Pearl

Abstract readReview
In one paragraph

Review in Bioscience reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
44citing papers in PubMed, 1 pooled it
–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

44 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
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  6. Article
  7. Review
  8. Article
  9. Article
  10. AI-Based Drug Discovery and Design for Different Genetic Designs.Methods in molecular biology (Clifton, N.J.) · 2025
    Article
  11. Article
  12. Article
  13. Computational Approaches: A New Frontier in Cancer Research.Combinatorial chemistry & high throughput screening · 2024
    Review
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  15. Article
  16. Review
  17. Article
  18. Article
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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.

Sarah K WoollerSchool of Life Sciences, University of Sussex, Falmer, Brighton BN1 9QJ, U.K.
Graeme Benstead-HumeSchool of Life Sciences, University of Sussex, Falmer, Brighton BN1 9QJ, U.K.
Xiangrong ChenSchool of Life Sciences, University of Sussex, Falmer, Brighton BN1 9QJ, U.K.
Yusuf AliSchool of Life Sciences, University of Sussex, Falmer, Brighton BN1 9QJ, U.K.
Frances M G PearlSchool of Life Sciences, University of Sussex, Falmer, Brighton BN1 9QJ, U.K. f.pearl@sussex.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Bioinformatics approaches are becoming ever more essential in translational drug discovery both in academia and within the pharmaceutical industry. Computational exploitation of the increasing volumes of data generated during all phases of drug discovery is enabling key challenges of the process to be addressed. Here, we highlight some of the areas in which bioinformatics resources and methods are being developed to support the drug discovery pipeline. These include the creation of large data warehouses, bioinformatics algorithms to analyse 'big data' that identify novel drug targets and/or biomarkers, programs to assess the tractability of targets, and prediction of repositioning opportunities that use licensed drugs to treat additional indications.

Indexed as

AnimalsComputational BiologyDrug DiscoveryHumanscomputational biochemistrydrug discovery and designgenomics

Identifiers

PMID28487472
PMCPMC6448364

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.