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HiPub: translating PubMed and PMC texts to networks for knowledge discovery

Overview of attention for article published in Bioinformatics, August 2016
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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 (96th percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

Mentioned by

news
7 news outlets
blogs
1 blog
twitter
21 tweeters
facebook
1 Facebook page
googleplus
1 Google+ user

Citations

dimensions_citation
13 Dimensions

Readers on

mendeley
48 Mendeley
citeulike
1 CiteULike
Title
HiPub: translating PubMed and PMC texts to networks for knowledge discovery
Published in
Bioinformatics, August 2016
DOI 10.1093/bioinformatics/btw511
Pubmed ID
Authors

Kyubum Lee, Wonho Shin, Byounggun Kim, Sunwon Lee, Yonghwa Choi, Sunkyu Kim, Minji Jeon, Aik Choon Tan, Jaewoo Kang

Abstract

We introduce HiPub, a seamless Chrome browser plug-in that automatically recognizes, annotates and translates biomedical entities from texts into networks for knowledge discovery. Using a combination of two different named-entity recognition resources, HiPub can recognize genes, proteins, diseases, drugs, mutations and cell lines in texts, and achieve high precision and recall. HiPub extracts biomedical entity-relationships from texts to construct context-specific networks, and integrates existing network data from external databases for knowledge discovery. It allows users to add additional entities from related articles, as well as user-defined entities for discovering new and unexpected entity-relationships. HiPub provides functional enrichment analysis on the biomedical entity network, and link-outs to external resources to assist users in learning new entities and relations. HiPub and detailed user guide are available at http://hipub.korea.ac.kr CONTACT: kangj@korea.ac.kr, aikchoon.tan@ucdenver.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Twitter Demographics

The data shown below were collected from the profiles of 21 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 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 2%
France 1 2%
Germany 1 2%
Unknown 45 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 29%
Student > Ph. D. Student 10 21%
Student > Master 8 17%
Professor 4 8%
Professor > Associate Professor 3 6%
Other 6 13%
Unknown 3 6%
Readers by discipline Count As %
Computer Science 15 31%
Biochemistry, Genetics and Molecular Biology 9 19%
Agricultural and Biological Sciences 8 17%
Medicine and Dentistry 2 4%
Social Sciences 2 4%
Other 7 15%
Unknown 5 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 69. 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 05 January 2017.
All research outputs
#298,597
of 15,060,518 outputs
Outputs from Bioinformatics
#55
of 9,659 outputs
Outputs of similar age
#9,337
of 266,172 outputs
Outputs of similar age from Bioinformatics
#4
of 306 outputs
Altmetric has tracked 15,060,518 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,659 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.2. This one has done particularly well, scoring higher than 99% 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 266,172 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 96% of its contemporaries.
We're also able to compare this research output to 306 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 98% of its contemporaries.