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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Improving clustering with metabolic pathway data
|
|---|---|
| Published in |
BMC Bioinformatics, April 2014
|
| DOI | 10.1186/1471-2105-15-101 |
| Pubmed ID | |
| Authors |
Diego H Milone, Georgina Stegmayer, Mariana López, Laura Kamenetzky, Fernando Carrari |
| Abstract |
It is a common practice in bioinformatics to validate each group returned by a clustering algorithm through manual analysis, according to a-priori biological knowledge. This procedure helps finding functionally related patterns to propose hypotheses for their behavior and the biological processes involved. Therefore, this knowledge is used only as a second step, after data are just clustered according to their expression patterns. Thus, it could be very useful to be able to improve the clustering of biological data by incorporating prior knowledge into the cluster formation itself, in order to enhance the biological value of the clusters. |
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 % |
|---|---|---|
| United States | 2 | 67% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 5% |
| Germany | 1 | 2% |
| Brazil | 1 | 2% |
| Netherlands | 1 | 2% |
| Belarus | 1 | 2% |
| Israel | 1 | 2% |
| Unknown | 36 | 84% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 10 | 23% |
| Student > Ph. D. Student | 7 | 16% |
| Student > Bachelor | 4 | 9% |
| Student > Doctoral Student | 4 | 9% |
| Student > Master | 4 | 9% |
| Other | 10 | 23% |
| Unknown | 4 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 18 | 42% |
| Computer Science | 12 | 28% |
| Biochemistry, Genetics and Molecular Biology | 3 | 7% |
| Mathematics | 2 | 5% |
| Psychology | 1 | 2% |
| Other | 3 | 7% |
| Unknown | 4 | 9% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 May 2014.
All research outputs
#14,779,591
of 22,753,345 outputs
Outputs from BMC Bioinformatics
#5,041
of 7,269 outputs
Outputs of similar age
#128,915
of 228,161 outputs
Outputs of similar age from BMC Bioinformatics
#67
of 118 outputs
Altmetric has tracked 22,753,345 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,269 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 26th percentile – i.e., 26% 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 228,161 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 118 others from the same source and published within six weeks on either side of this one. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.