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IntPath--an integrated pathway gene relationship database for model organisms and important pathogens

Overview of attention for article published in BMC Systems Biology, December 2012
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (52nd percentile)
  • Good Attention Score compared to outputs of the same age and source (71st percentile)

Mentioned by

twitter
1 tweeter
weibo
1 weibo user

Citations

dimensions_citation
37 Dimensions

Readers on

mendeley
52 Mendeley
citeulike
2 CiteULike
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Title
IntPath--an integrated pathway gene relationship database for model organisms and important pathogens
Published in
BMC Systems Biology, December 2012
DOI 10.1186/1752-0509-6-s2-s2
Pubmed ID
Authors

Hufeng Zhou, Jingjing Jin, Haojun Zhang, Bo Yi, Michal Wozniak, Limsoon Wong

Abstract

Pathway data are important for understanding the relationship between genes, proteins and many other molecules in living organisms. Pathway gene relationships are crucial information for guidance, prediction, reference and assessment in biochemistry, computational biology, and medicine. Many well-established databases--e.g., KEGG, WikiPathways, and BioCyc--are dedicated to collecting pathway data for public access. However, the effectiveness of these databases is hindered by issues such as incompatible data formats, inconsistent molecular representations, inconsistent molecular relationship representations, inconsistent referrals to pathway names, and incomprehensive data from different databases.

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 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Singapore 2 4%
United States 1 2%
United Kingdom 1 2%
Unknown 48 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 27%
Researcher 11 21%
Student > Master 10 19%
Student > Bachelor 6 12%
Other 3 6%
Other 5 10%
Unknown 3 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 16 31%
Computer Science 14 27%
Biochemistry, Genetics and Molecular Biology 8 15%
Medicine and Dentistry 5 10%
Business, Management and Accounting 1 2%
Other 2 4%
Unknown 6 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 September 2014.
All research outputs
#10,787,576
of 18,796,975 outputs
Outputs from BMC Systems Biology
#454
of 1,127 outputs
Outputs of similar age
#92,743
of 202,077 outputs
Outputs of similar age from BMC Systems Biology
#11
of 35 outputs
Altmetric has tracked 18,796,975 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,127 research outputs from this source. They receive a mean Attention Score of 3.5. This one has gotten more attention than average, scoring higher than 56% 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 202,077 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.
We're also able to compare this research output to 35 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.