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Mango: combining and analyzing heterogeneous biological networks

Overview of attention for article published in BioData Mining, August 2016
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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 (84th percentile)

Mentioned by

twitter
18 tweeters

Citations

dimensions_citation
2 Dimensions

Readers on

mendeley
34 Mendeley
citeulike
3 CiteULike
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Title
Mango: combining and analyzing heterogeneous biological networks
Published in
BioData Mining, August 2016
DOI 10.1186/s13040-016-0105-5
Pubmed ID
Authors

Jennifer Chang, Hyejin Cho, Hui-Hsien Chou

Abstract

Heterogeneous biological data such as sequence matches, gene expression correlations, protein-protein interactions, and biochemical pathways can be merged and analyzed via graphs, or networks. Existing software for network analysis has limited scalability to large data sets or is only accessible to software developers as libraries. In addition, the polymorphic nature of the data sets requires a more standardized method for integration and exploration. Mango facilitates large network analyses with its Graph Exploration Language, automatic graph attribute handling, and real-time 3-dimensional visualization. On a personal computer Mango can load, merge, and analyze networks with millions of links and can connect to online databases to fetch and merge biological pathways. Mango is written in C++ and runs on Mac OS, Windows, and Linux. The stand-alone distributions, including the Graph Exploration Language integrated development environment, are freely available for download from http://www.complex.iastate.edu/download/Mango. The Mango User Guide listing all features can be found at http://www.gitbook.com/book/j23414/mango-user-guide.

Twitter Demographics

The data shown below were collected from the profiles of 18 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 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Cuba 1 3%
United States 1 3%
New Caledonia 1 3%
Singapore 1 3%
Unknown 30 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 29%
Researcher 9 26%
Professor 3 9%
Student > Bachelor 3 9%
Student > Postgraduate 2 6%
Other 5 15%
Unknown 2 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 32%
Biochemistry, Genetics and Molecular Biology 8 24%
Computer Science 4 12%
Medicine and Dentistry 3 9%
Unspecified 1 3%
Other 2 6%
Unknown 5 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 17 October 2016.
All research outputs
#1,724,793
of 15,031,023 outputs
Outputs from BioData Mining
#56
of 244 outputs
Outputs of similar age
#40,722
of 266,406 outputs
Outputs of similar age from BioData Mining
#1
of 1 outputs
Altmetric has tracked 15,031,023 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 244 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.5. This one has done well, scoring higher than 76% 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 266,406 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 84% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them