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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 (94th percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

Mentioned by

blogs
1 blog
twitter
3 X users
patent
6 patents
reddit
1 Redditor

Citations

dimensions_citation
1050 Dimensions

Readers on

mendeley
705 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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 705 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 10 1%
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 659 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 164 23%
Researcher 150 21%
Student > Master 78 11%
Student > Bachelor 46 7%
Student > Doctoral Student 37 5%
Other 113 16%
Unknown 117 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 179 25%
Biochemistry, Genetics and Molecular Biology 126 18%
Medicine and Dentistry 61 9%
Computer Science 60 9%
Mathematics 23 3%
Other 114 16%
Unknown 142 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 23. 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 February 2023.
All research outputs
#1,466,811
of 23,410,748 outputs
Outputs from BMC Bioinformatics
#260
of 7,380 outputs
Outputs of similar age
#4,810
of 95,256 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 65 outputs
Altmetric has tracked 23,410,748 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 7,380 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 96% 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 95,256 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 94% of its contemporaries.
We're also able to compare this research output to 65 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 98% of its contemporaries.