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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

  • Above-average Attention Score compared to outputs of the same age (61st percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

Mentioned by

patent
2 patents

Citations

dimensions_citation
77 Dimensions

Readers on

mendeley
127 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

Mendeley readers

The data shown below were compiled from readership statistics for 127 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 2%
Denmark 2 2%
Australia 1 <1%
Spain 1 <1%
Israel 1 <1%
Unknown 117 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 31 24%
Researcher 30 24%
Student > Master 18 14%
Student > Doctoral Student 8 6%
Professor > Associate Professor 8 6%
Other 15 12%
Unknown 17 13%
Readers by discipline Count As %
Computer Science 18 14%
Agricultural and Biological Sciences 18 14%
Engineering 15 12%
Biochemistry, Genetics and Molecular Biology 11 9%
Environmental Science 7 6%
Other 37 29%
Unknown 21 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 09 December 2010.
All research outputs
#3,577,528
of 12,373,386 outputs
Outputs from BMC Bioinformatics
#1,717
of 4,576 outputs
Outputs of similar age
#102,478
of 273,177 outputs
Outputs of similar age from BMC Bioinformatics
#4
of 24 outputs
Altmetric has tracked 12,373,386 research outputs across all sources so far. This one is in the 49th percentile – i.e., 49% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,576 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 53% 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 273,177 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 61% of its contemporaries.
We're also able to compare this research output to 24 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.