Evidence map›Paper›PMID 30840053›Full record

ArticleBioinformatics (Oxford, England)2019

A new computational drug repurposing method using established disease-drug pair knowledge.

Nafiseh Saberian, Azam Peyvandipour, Michele Donato, Sahar Ansari, Sorin Draghici

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

21 citing papers in PubMed.

  1. Article
  2. Article
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  4. Drug Repurposing: A Leading Strategy for New Threats and Targets.ACS pharmacology & translational science · 2024
    Review
  5. A drug repurposing method based on inhibition effect on gene regulatory network.Computational and structural biotechnology journal · 2023
    Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Shotgun drug repurposing biotechnology to tackle epidemics and pandemics.ChemRxiv : the preprint server for chemistry · 2020
    Article
  20. Review
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.

Nafiseh SaberianDepartment of Computer Science, Wayne State University, Detroit, MI, USA.
Azam PeyvandipourDepartment of Computer Science, Wayne State University, Detroit, MI, USA.
Michele DonatoDepartment of Computer Science, Wayne State University, Detroit, MI, USA.
Sahar AnsariDepartment of Computer Science, Wayne State University, Detroit, MI, USA.
Sorin DraghiciDepartment of Computer Science, Wayne State University, Detroit, MI, USA.

Funding

Pathway-Guide: A novel tool for the analysis of signaling and metabolic pathwaysR42GM087013 · NIGMS · ADVAITA CORPORATION · PI DRAGHICI, SORIN · 2011 to 2013
$2.2M
Novel methods for the analysis of gene signaling pathways with applications in obR01DK089167 · NIDDK · WAYNE STATE UNIVERSITY · PI DRAGHICI, SORIN · 2010 to 2013
$1.2M
NIDDK NIH HHS R01 DK089167NIGMS NIH HHS R42 GM087013
6 · The paper itself

Abstract

motivationDrug repurposing is a potential alternative to the classical drug discovery pipeline. Repurposing involves finding novel indications for already approved drugs. In this work, we present a novel machine learning-based method for drug repurposing. This method explores the anti-similarity between drugs and a disease to uncover new uses for the drugs. More specifically, our proposed method takes into account three sources of information: (i) large-scale gene expression profiles corresponding to human cell lines treated with small molecules, (ii) gene expression profile of a human disease and (iii) the known relationship between Food and Drug Administration (FDA)-approved drugs and diseases. Using these data, our proposed method learns a similarity metric through a supervised machine learning-based algorithm such that a disease and its associated FDA-approved drugs have smaller distance than the other disease-drug pairs.

resultsWe validated our framework by showing that the proposed method incorporating distance metric learning technique can retrieve FDA-approved drugs for their approved indications. Once validated, we used our approach to identify a few strong candidates for repurposing. AVAILABILITY AND IMPLEMENTATION: The R scripts are available on demand from the authors. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

Drug RepositioningAlgorithmsComputational BiologyDrug DiscoveryHumansMachine LearningPharmaceutical PreparationsPharmaceutical Preparations

Identifiers

PMID30840053
PMCPMC6761937

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.