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Mendeley readers
Attention Score in Context
| Title |
Data reduction for spectral clustering to analyze high throughput flow cytometry data
|
|---|---|
| Published in |
BMC Bioinformatics, July 2010
|
| DOI | 10.1186/1471-2105-11-403 |
| Pubmed ID | |
| Authors |
Habil Zare, Parisa Shooshtari, Arvind Gupta, Ryan R Brinkman |
| Abstract |
Recent biological discoveries have shown that clustering large datasets is essential for better understanding biology in many areas. Spectral clustering in particular has proven to be a powerful tool amenable for many applications. However, it cannot be directly applied to large datasets due to time and memory limitations. To address this issue, we have modified spectral clustering by adding an information preserving sampling procedure and applying a post-processing stage. We call this entire algorithm SamSPECTRAL. |
Mendeley readers
The data shown below were compiled from readership statistics for 171 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 2% |
| United Kingdom | 2 | 1% |
| Portugal | 1 | <1% |
| Sweden | 1 | <1% |
| Switzerland | 1 | <1% |
| Spain | 1 | <1% |
| France | 1 | <1% |
| Unknown | 160 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 41 | 24% |
| Researcher | 41 | 24% |
| Student > Master | 18 | 11% |
| Student > Doctoral Student | 12 | 7% |
| Professor > Associate Professor | 9 | 5% |
| Other | 30 | 18% |
| Unknown | 20 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 45 | 26% |
| Computer Science | 32 | 19% |
| Biochemistry, Genetics and Molecular Biology | 18 | 11% |
| Engineering | 13 | 8% |
| Medicine and Dentistry | 9 | 5% |
| Other | 30 | 18% |
| Unknown | 24 | 14% |
Attention Score in Context
This research output has an Altmetric Attention Score of 19. 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 01 March 2023.
All research outputs
#1,684,827
of 23,462,326 outputs
Outputs from BMC Bioinformatics
#356
of 7,391 outputs
Outputs of similar age
#5,748
of 95,585 outputs
Outputs of similar age from BMC Bioinformatics
#2
of 56 outputs
Altmetric has tracked 23,462,326 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,391 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done particularly well, scoring higher than 95% 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 95,585 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 93% of its contemporaries.
We're also able to compare this research output to 56 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.