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X Demographics
Mendeley readers
Attention Score in Context
| 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
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 718 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 | 672 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 164 | 23% |
| Researcher | 152 | 21% |
| Student > Master | 78 | 11% |
| Student > Bachelor | 46 | 6% |
| Student > Doctoral Student | 38 | 5% |
| Other | 113 | 16% |
| Unknown | 127 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 180 | 25% |
| Biochemistry, Genetics and Molecular Biology | 127 | 18% |
| Medicine and Dentistry | 62 | 9% |
| Computer Science | 61 | 8% |
| Mathematics | 23 | 3% |
| Other | 112 | 16% |
| Unknown | 153 | 21% |
Attention Score in Context
This research output has an Altmetric Attention Score of 28. 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 22 April 2024.
All research outputs
#1,431,085
of 25,874,560 outputs
Outputs from BMC Bioinformatics
#176
of 7,759 outputs
Outputs of similar age
#4,489
of 106,285 outputs
Outputs of similar age from BMC Bioinformatics
#2
of 63 outputs
Altmetric has tracked 25,874,560 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,759 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.6. This one has done particularly well, scoring higher than 97% 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 106,285 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 95% of its contemporaries.
We're also able to compare this research output to 63 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 96% of its contemporaries.