↓ Skip to main content

DGIdb 2.0: mining clinically relevant drug–gene interactions

Overview of attention for article published in Nucleic Acids Research, November 2015
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (91st percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

Mentioned by

blogs
1 blog
twitter
19 tweeters
googleplus
1 Google+ user

Readers on

mendeley
110 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
DGIdb 2.0: mining clinically relevant drug–gene interactions
Published in
Nucleic Acids Research, November 2015
DOI 10.1093/nar/gkv1165
Pubmed ID
Authors

Alex H. Wagner, Adam C. Coffman, Benjamin J. Ainscough, Nicholas C. Spies, Zachary L. Skidmore, Katie M. Campbell, Kilannin Krysiak, Deng Pan, Joshua F. McMichael, James M. Eldred, Jason R. Walker, Richard K. Wilson, Elaine R. Mardis, Malachi Griffith, Obi L. Griffith, Wagner, Alex H, Coffman, Adam C, Ainscough, Benjamin J, Spies, Nicholas C, Skidmore, Zachary L, Campbell, Katie M, Krysiak, Kilannin, Pan, Deng, McMichael, Joshua F, Eldred, James M, Walker, Jason R, Wilson, Richard K, Mardis, Elaine R, Griffith, Malachi, Griffith, Obi L

Abstract

The Drug-Gene Interaction Database (DGIdb, www.dgidb.org) is a web resource that consolidates disparate data sources describing drug-gene interactions and gene druggability. It provides an intuitive graphical user interface and a documented application programming interface (API) for querying these data. DGIdb was assembled through an extensive manual curation effort, reflecting the combined information of twenty-seven sources. For DGIdb 2.0, substantial updates have been made to increase content and improve its usefulness as a resource for mining clinically actionable drug targets. Specifically, nine new sources of drug-gene interactions have been added, including seven resources specifically focused on interactions linked to clinical trials. These additions have more than doubled the overall count of drug-gene interactions. The total number of druggable gene claims has also increased by 30%. Importantly, a majority of the unrestricted, publicly-accessible sources used in DGIdb are now automatically updated on a weekly basis, providing the most current information for these sources. Finally, a new web view and API have been developed to allow searching for interactions by drug identifiers to complement existing gene-based search functionality. With these updates, DGIdb represents a comprehensive and user friendly tool for mining the druggable genome for precision medicine hypothesis generation.

Twitter Demographics

The data shown below were collected from the profiles of 19 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 110 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 2 2%
United States 2 2%
Canada 2 2%
Portugal 1 <1%
Switzerland 1 <1%
France 1 <1%
Italy 1 <1%
United Kingdom 1 <1%
China 1 <1%
Other 3 3%
Unknown 95 86%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 36 33%
Researcher 28 25%
Student > Master 17 15%
Student > Bachelor 9 8%
Professor 5 5%
Other 15 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 44 40%
Biochemistry, Genetics and Molecular Biology 27 25%
Medicine and Dentistry 13 12%
Computer Science 9 8%
Pharmacology, Toxicology and Pharmaceutical Science 4 4%
Other 13 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 19. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 January 2016.
All research outputs
#451,915
of 8,199,493 outputs
Outputs from Nucleic Acids Research
#363
of 10,787 outputs
Outputs of similar age
#20,594
of 244,734 outputs
Outputs of similar age from Nucleic Acids Research
#24
of 361 outputs
Altmetric has tracked 8,199,493 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 10,787 research outputs from this source. They receive a mean Attention Score of 4.4. This one has done particularly well, scoring higher than 96% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 244,734 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 361 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 93% of its contemporaries.