Evidence map›Paper›PMID 26860211›Full record

ArticleBMC bioinformatics2016

ksRepo: a generalized platform for computational drug repositioning.

Adam S Brown, Sek Won Kong, Isaac S Kohane, Chirag J Patel

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 1 pooled it
9.4field-weighted citation impact, top 2% 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

21 citing papers in PubMed, 1 synthesis or guideline pooled it, 57 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Frontiers in pharmacology · 2024
    Article
  5. Review
  6. Review
  7. Article
  8. Review
  9. Article
  10. Article
  11. Machine learning applications in drug development.Computational and structural biotechnology journal · 2020
    Review
  12. Review
  13. Review
  14. Changing Trends in Computational Drug Repositioning.Pharmaceuticals (Basel, Switzerland) · 2018
    Review
  15. Article
  16. Observational
  17. Review
  18. Article
  19. MeSHDD: Literature-based drug-drug similarity for drug repositioning.Journal of the American Medical Informatics Association : JAMIA · 2017
    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

4 authors at 2 institutions in 1 country.

Adam S BrownDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA. adambrown@fas.harvard.edu.
Sek Won KongBoston Children's Hospital, Boston, MA, 02115, USA. sekwon.kong@childrens.harvard.edu.
Isaac S KohaneDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA. isaac_kohane@harvard.edu.
Chirag J PatelDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA. Chirag_Patel@hms.harvard.edu.
Harvard University · USBoston Children's Hospital · US

Funding

TrainingU54HG007963 · NHGRI · HARVARD MEDICAL SCHOOL · PI KOHANE, ISAAC S. · 2014 to 2018
$18.3M
Training in Pharmacological SciencesT32GM007306 · NIGMS · HARVARD UNIVERSITY (MEDICAL SCHOOL) · PI GOLAN, DAVID E. · 1985 to 2018
$6.6M
An environment-wide association study in autism spectrum disorders using novel bioinformatics methods and metabolomics via mass spectrometryR01MH107205 · NIMH · BOSTON CHILDREN'S HOSPITAL · PI KONG, SEK WON · 2015 to 2019
$2.1M
Data-driven identification of environmental factors in cardiovascular diseaseR00ES023504 · NIEHS · HARVARD MEDICAL SCHOOL · PI PATEL, CHIRAG J. · 2016 to 2018
$728k
Increasing the power of GxE detection by using multi-locus genome-wide predictorsR21ES025052 · NIEHS · HARVARD MEDICAL SCHOOL · PI PATEL, CHIRAG J. · 2015 to 2017
$434k
Data-driven identification of environmental factors in cardiovascular diseaseK99ES023504 · NIEHS · HARVARD MEDICAL SCHOOL · PI PATEL, CHIRAG J. · 2014 to 2015
$245k
NHGRI NIH HHS U54 HG007963NHGRI NIH HHS U54HG007963NIEHS NIH HHS K99 ES023504NIEHS NIH HHS K99ES023504NIEHS NIH HHS R00 ES023504NIEHS NIH HHS R21 ES025052NIGMS NIH HHS T32 GM007306NIGMS NIH HHS T32 GM007306-39NIMH NIH HHS R01 MH107205
6 · The paper itself

Abstract

backgroundRepositioning approved drug and small molecules in novel therapeutic areas is of key interest to the pharmaceutical industry. A number of promising computational techniques have been developed to aid in repositioning, however, the majority of available methodologies require highly specific data inputs that preclude the use of many datasets and databases. There is a clear unmet need for a generalized methodology that enables the integration of multiple types of both gene expression data and database schema.

resultsksRepo eliminates the need for a single microarray platform as input and allows for the use of a variety of drug and chemical exposure databases. We tested ksRepo's performance on a set of five prostate cancer datasets using the Comparative Toxicogenomics Database (CTD) as our database of gene-compound interactions. ksRepo successfully predicted significance for five frontline prostate cancer therapies, representing a significant enrichment from over 7000 CTD compounds, and achieved specificity similar to other repositioning methods.

conclusionsWe present ksRepo, which enables investigators to use any data inputs for computational drug repositioning. ksRepo is implemented in a series of four functions in the R statistical environment under a BSD3 license. Source code is freely available at http://github.com/adam-sam-brown/ksRepo. A vignette is provided to aid users in performing ksRepo analysis.

Indexed as

Databases, FactualSoftwareComputational BiologyComputer SimulationDrug RepositioningHumansMaleModels, TheoreticalPharmaceutical PreparationsProstatic NeoplasmsToxicogeneticsPharmaceutical Preparations

Identifiers

PMID26860211
PMCPMC4746802
OpenAlexW2255438499

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.