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LS-NMF: A modified non-negative matrix factorization algorithm utilizing uncertainty estimates

Overview of attention for article published in BMC Bioinformatics, March 2006
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (65th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (55th percentile)

Mentioned by

twitter
1 X user
patent
2 patents

Citations

dimensions_citation
85 Dimensions

Readers on

mendeley
135 Mendeley
citeulike
4 CiteULike
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Title
LS-NMF: A modified non-negative matrix factorization algorithm utilizing uncertainty estimates
Published in
BMC Bioinformatics, March 2006
DOI 10.1186/1471-2105-7-175
Pubmed ID
Authors

Guoli Wang, Andrew V Kossenkov, Michael F Ochs

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 135 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 2%
China 2 1%
Denmark 2 1%
Australia 1 <1%
Spain 1 <1%
Israel 1 <1%
Unknown 125 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 32 24%
Student > Ph. D. Student 31 23%
Student > Master 18 13%
Student > Doctoral Student 8 6%
Professor > Associate Professor 8 6%
Other 18 13%
Unknown 20 15%
Readers by discipline Count As %
Computer Science 19 14%
Agricultural and Biological Sciences 18 13%
Engineering 15 11%
Biochemistry, Genetics and Molecular Biology 11 8%
Medicine and Dentistry 7 5%
Other 41 30%
Unknown 24 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 02 June 2023.
All research outputs
#7,778,943
of 24,938,276 outputs
Outputs from BMC Bioinformatics
#2,842
of 7,613 outputs
Outputs of similar age
#25,938
of 78,243 outputs
Outputs of similar age from BMC Bioinformatics
#25
of 56 outputs
Altmetric has tracked 24,938,276 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 7,613 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has gotten more attention than average, scoring higher than 60% 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 78,243 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 65% of its contemporaries.
We're also able to compare this research output to 56 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 55% of its contemporaries.