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atBioNet– an integrated network analysis tool for genomics and biomarker discovery

Overview of attention for article published in BMC Genomics, July 2012
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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 (88th percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

Mentioned by

blogs
1 blog
twitter
1 tweeter

Citations

dimensions_citation
30 Dimensions

Readers on

mendeley
54 Mendeley
citeulike
2 CiteULike
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Title
atBioNet– an integrated network analysis tool for genomics and biomarker discovery
Published in
BMC Genomics, July 2012
DOI 10.1186/1471-2164-13-325
Pubmed ID
Authors

Yijun Ding, Minjun Chen, Zhichao Liu, Don Ding, Yanbin Ye, Min Zhang, Reagan Kelly, Li Guo, Zhenqiang Su, Stephen C Harris, Feng Qian, Weigong Ge, Hong Fang, Xiaowei Xu, Weida Tong

Abstract

Large amounts of mammalian protein-protein interaction (PPI) data have been generated and are available for public use. From a systems biology perspective, Proteins/genes interactions encode the key mechanisms distinguishing disease and health, and such mechanisms can be uncovered through network analysis. An effective network analysis tool should integrate different content-specific PPI databases into a comprehensive network format with a user-friendly platform to identify key functional modules/pathways and the underlying mechanisms of disease and toxicity.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Hungary 1 2%
Germany 1 2%
France 1 2%
Mexico 1 2%
United States 1 2%
Unknown 49 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 24%
Student > Ph. D. Student 13 24%
Student > Bachelor 6 11%
Professor > Associate Professor 4 7%
Student > Postgraduate 4 7%
Other 11 20%
Unknown 3 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 21 39%
Computer Science 8 15%
Biochemistry, Genetics and Molecular Biology 7 13%
Medicine and Dentistry 5 9%
Engineering 3 6%
Other 5 9%
Unknown 5 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 10 August 2012.
All research outputs
#1,403,318
of 12,373,620 outputs
Outputs from BMC Genomics
#756
of 7,313 outputs
Outputs of similar age
#14,099
of 121,540 outputs
Outputs of similar age from BMC Genomics
#6
of 71 outputs
Altmetric has tracked 12,373,620 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,313 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done well, scoring higher than 89% 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 121,540 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 88% of its contemporaries.
We're also able to compare this research output to 71 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 92% of its contemporaries.