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Mendeley readers
Attention Score in Context
Title |
Non-negative matrix factorization by maximizing correntropy for cancer clustering
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Published in |
BMC Bioinformatics, March 2013
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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. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
India | 2 | 50% |
Norway | 1 | 25% |
United States | 1 | 25% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 3 | 75% |
Scientists | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 70 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% |
Ireland | 1 | 1% |
Italy | 1 | 1% |
South Africa | 1 | 1% |
Sweden | 1 | 1% |
Japan | 1 | 1% |
China | 1 | 1% |
Unknown | 60 | 86% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 21 | 30% |
Researcher | 15 | 21% |
Student > Master | 12 | 17% |
Professor > Associate Professor | 4 | 6% |
Other | 4 | 6% |
Other | 8 | 11% |
Unknown | 6 | 9% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 24 | 34% |
Agricultural and Biological Sciences | 9 | 13% |
Engineering | 8 | 11% |
Mathematics | 7 | 10% |
Biochemistry, Genetics and Molecular Biology | 7 | 10% |
Other | 8 | 11% |
Unknown | 7 | 10% |
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
#12,679,392
of 22,701,287 outputs
Outputs from BMC Bioinformatics
#3,620
of 7,254 outputs
Outputs of similar age
#99,506
of 197,397 outputs
Outputs of similar age from BMC Bioinformatics
#74
of 147 outputs
Altmetric has tracked 22,701,287 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,254 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 48th percentile – i.e., 48% of its peers scored the same or lower than it.
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 197,397 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 147 others from the same source and published within six weeks on either side of this one. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.