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Non-negative matrix factorization by maximizing correntropy for cancer clustering

Overview of attention for article published in BMC Bioinformatics, January 2013
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (60th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
4 tweeters

Citations

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77 Dimensions

Readers on

mendeley
64 Mendeley
citeulike
1 CiteULike
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Title
Non-negative matrix factorization by maximizing correntropy for cancer clustering
Published in
BMC Bioinformatics, January 2013
DOI 10.1186/1471-2105-14-107
Pubmed ID
Authors

Jim Jing-Yan Wang, Xiaolei Wang, Xin Gao

Abstract

Non-negative matrix factorization (NMF) has been shown to be a powerful tool for clustering gene expression data, which are widely used to classify cancers. NMF aims to find two non-negative matrices whose product closely approximates the original matrix. Traditional NMF methods minimize either the l2 norm or the Kullback-Leibler distance between the product of the two matrices and the original matrix. Correntropy was recently shown to be an effective similarity measurement due to its stability to outliers or noise.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 64 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Chile 2 3%
United States 2 3%
Italy 1 2%
Ireland 1 2%
South Africa 1 2%
China 1 2%
Japan 1 2%
Sweden 1 2%
Unknown 54 84%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 33%
Researcher 15 23%
Student > Master 9 14%
Other 4 6%
Professor > Associate Professor 4 6%
Other 7 11%
Unknown 4 6%
Readers by discipline Count As %
Computer Science 22 34%
Agricultural and Biological Sciences 9 14%
Engineering 8 13%
Mathematics 7 11%
Biochemistry, Genetics and Molecular Biology 6 9%
Other 7 11%
Unknown 5 8%

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 24 March 2013.
All research outputs
#4,450,635
of 14,571,674 outputs
Outputs from BMC Bioinformatics
#2,059
of 5,418 outputs
Outputs of similar age
#45,641
of 149,565 outputs
Outputs of similar age from BMC Bioinformatics
#10
of 19 outputs
Altmetric has tracked 14,571,674 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 5,418 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 149,565 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 60% of its contemporaries.
We're also able to compare this research output to 19 others from the same source and published within six weeks on either side of this one. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.