↓ 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 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

Mentioned by

blogs
1 blog
twitter
19 tweeters
wikipedia
1 Wikipedia page
googleplus
1 Google+ user

Readers on

mendeley
127 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 127 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 2%
Unknown 112 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 37 29%
Researcher 35 28%
Student > Master 19 15%
Student > Bachelor 13 10%
Professor 5 4%
Other 18 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 54 43%
Biochemistry, Genetics and Molecular Biology 28 22%
Medicine and Dentistry 12 9%
Computer Science 10 8%
Unspecified 6 5%
Other 17 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 22. 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 18 February 2018.
All research outputs
#512,763
of 10,490,471 outputs
Outputs from Nucleic Acids Research
#359
of 17,003 outputs
Outputs of similar age
#19,098
of 250,693 outputs
Outputs of similar age from Nucleic Acids Research
#17
of 363 outputs
Altmetric has tracked 10,490,471 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 17,003 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done particularly well, scoring higher than 97% 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 250,693 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 92% of its contemporaries.
We're also able to compare this research output to 363 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 95% of its contemporaries.