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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 (96th percentile)

Mentioned by

blogs
1 blog
twitter
3 tweeters
patent
3 patents
reddit
1 Redditor

Citations

dimensions_citation
744 Dimensions

Readers on

mendeley
626 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 626 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 10 2%
United States 9 1%
Spain 3 <1%
Germany 2 <1%
Italy 2 <1%
Canada 2 <1%
Australia 2 <1%
South Africa 2 <1%
Brazil 2 <1%
Other 12 2%
Unknown 580 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 154 25%
Researcher 145 23%
Student > Master 72 12%
Student > Bachelor 39 6%
Student > Doctoral Student 32 5%
Other 106 17%
Unknown 78 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 175 28%
Biochemistry, Genetics and Molecular Biology 110 18%
Computer Science 57 9%
Medicine and Dentistry 54 9%
Mathematics 22 4%
Other 110 18%
Unknown 98 16%

Attention Score in Context

This research output has an Altmetric Attention Score of 20. 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 24 November 2021.
All research outputs
#1,455,443
of 21,252,842 outputs
Outputs from BMC Bioinformatics
#323
of 6,902 outputs
Outputs of similar age
#12,450
of 173,085 outputs
Outputs of similar age from BMC Bioinformatics
#2
of 33 outputs
Altmetric has tracked 21,252,842 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 6,902 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. 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 173,085 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 33 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 96% of its contemporaries.