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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Computational cluster validation for microarray data analysis: experimental assessment of Clest, Consensus Clustering, Figure of Merit, Gap Statistics and Model Explorer
|
|---|---|
| Published in |
BMC Bioinformatics, October 2008
|
| DOI | 10.1186/1471-2105-9-462 |
| Pubmed ID | |
| Authors |
Raffaele Giancarlo, Davide Scaturro, Filippo Utro |
| Abstract |
Inferring cluster structure in microarray datasets is a fundamental task for the so-called -omic sciences. It is also a fundamental question in Statistics, Data Analysis and Classification, in particular with regard to the prediction of the number of clusters in a dataset, usually established via internal validation measures. Despite the wealth of internal measures available in the literature, new ones have been recently proposed, some of them specifically for microarray data. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 94 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 3% |
| France | 2 | 2% |
| Spain | 2 | 2% |
| Sweden | 1 | 1% |
| South Africa | 1 | 1% |
| Brazil | 1 | 1% |
| China | 1 | 1% |
| Germany | 1 | 1% |
| United Kingdom | 1 | 1% |
| Other | 1 | 1% |
| Unknown | 80 | 85% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 32 | 34% |
| Student > Ph. D. Student | 24 | 26% |
| Professor > Associate Professor | 11 | 12% |
| Other | 8 | 9% |
| Student > Master | 5 | 5% |
| Other | 9 | 10% |
| Unknown | 5 | 5% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 30 | 32% |
| Computer Science | 22 | 23% |
| Mathematics | 10 | 11% |
| Medicine and Dentistry | 9 | 10% |
| Biochemistry, Genetics and Molecular Biology | 6 | 6% |
| Other | 8 | 9% |
| Unknown | 9 | 10% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 23 November 2011.
All research outputs
#14,720,444
of 23,577,654 outputs
Outputs from BMC Bioinformatics
#4,813
of 7,400 outputs
Outputs of similar age
#77,509
of 92,860 outputs
Outputs of similar age from BMC Bioinformatics
#41
of 51 outputs
Altmetric has tracked 23,577,654 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,400 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 30th percentile – i.e., 30% 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 92,860 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 51 others from the same source and published within six weeks on either side of this one. This one is in the 19th percentile – i.e., 19% of its contemporaries scored the same or lower than it.