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A flexible R package for nonnegative matrix factorization

Overview of attention for article published in BMC Bioinformatics, July 2010
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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 (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Mentioned by

blogs
1 blog
twitter
3 tweeters
patent
2 patents

Readers on

mendeley
496 Mendeley
citeulike
6 CiteULike
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Title
A flexible R package for nonnegative matrix factorization
Published in
BMC Bioinformatics, July 2010
DOI 10.1186/1471-2105-11-367
Pubmed ID
Authors

Renaud Gaujoux, Cathal Seoighe

Abstract

Nonnegative Matrix Factorization (NMF) is an unsupervised learning technique that has been applied successfully in several fields, including signal processing, face recognition and text mining. Recent applications of NMF in bioinformatics have demonstrated its ability to extract meaningful information from high-dimensional data such as gene expression microarrays. Developments in NMF theory and applications have resulted in a variety of algorithms and methods. However, most NMF implementations have been on commercial platforms, while those that are freely available typically require programming skills. This limits their use by the wider research community.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 496 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 10 2%
United Kingdom 10 2%
Spain 3 <1%
Germany 2 <1%
Canada 2 <1%
South Africa 2 <1%
Italy 2 <1%
Australia 2 <1%
Brazil 2 <1%
Other 12 2%
Unknown 449 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 131 26%
Researcher 122 25%
Student > Master 55 11%
Professor > Associate Professor 29 6%
Student > Bachelor 28 6%
Other 87 18%
Unknown 44 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 160 32%
Biochemistry, Genetics and Molecular Biology 85 17%
Computer Science 57 11%
Medicine and Dentistry 38 8%
Mathematics 21 4%
Other 78 16%
Unknown 57 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 19. 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 23 October 2018.
All research outputs
#956,171
of 14,803,679 outputs
Outputs from BMC Bioinformatics
#234
of 5,496 outputs
Outputs of similar age
#10,898
of 152,937 outputs
Outputs of similar age from BMC Bioinformatics
#2
of 21 outputs
Altmetric has tracked 14,803,679 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,496 research outputs from this source. They receive a mean Attention Score of 5.0. This one has done particularly well, scoring higher than 95% 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 152,937 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 21 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 90% of its contemporaries.