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X Demographics
Mendeley readers
Attention Score in Context
| Title |
The non-negative matrix factorization toolbox for biological data mining
|
|---|---|
| Published in |
Source Code for Biology and Medicine, April 2013
|
| DOI | 10.1186/1751-0473-8-10 |
| Pubmed ID | |
| Authors |
Yifeng Li, Alioune Ngom |
| Abstract |
Non-negative matrix factorization (NMF) has been introduced as an important method for mining biological data. Though there currently exists packages implemented in R and other programming languages, they either provide only a few optimization algorithms or focus on a specific application field. There does not exist a complete NMF package for the bioinformatics community, and in order to perform various data mining tasks on biological data. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| China | 1 | 33% |
| Australia | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 243 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 2 | <1% |
| United States | 2 | <1% |
| Colombia | 1 | <1% |
| Italy | 1 | <1% |
| Israel | 1 | <1% |
| India | 1 | <1% |
| United Kingdom | 1 | <1% |
| South Africa | 1 | <1% |
| Denmark | 1 | <1% |
| Other | 3 | 1% |
| Unknown | 229 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 70 | 29% |
| Researcher | 49 | 20% |
| Student > Master | 31 | 13% |
| Student > Bachelor | 20 | 8% |
| Student > Doctoral Student | 12 | 5% |
| Other | 20 | 8% |
| Unknown | 41 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 47 | 19% |
| Engineering | 40 | 16% |
| Agricultural and Biological Sciences | 27 | 11% |
| Neuroscience | 19 | 8% |
| Biochemistry, Genetics and Molecular Biology | 14 | 6% |
| Other | 47 | 19% |
| Unknown | 49 | 20% |
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 17 April 2013.
All research outputs
#12,583,769
of 22,707,247 outputs
Outputs from Source Code for Biology and Medicine
#48
of 127 outputs
Outputs of similar age
#87,724
of 175,235 outputs
Outputs of similar age from Source Code for Biology and Medicine
#2
of 3 outputs
Altmetric has tracked 22,707,247 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 127 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one has gotten more attention than average, scoring higher than 61% 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 175,235 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 3 others from the same source and published within six weeks on either side of this one.