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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Parametric modeling of cellular state transitions as measured with flow cytometry
|
|---|---|
| Published in |
BMC Bioinformatics, April 2012
|
| DOI | 10.1186/1471-2105-13-s5-s5 |
| Pubmed ID | |
| Authors |
Hsiu J Ho, Tsung I Lin, Hannah H Chang, Steven B Haase, Sui Huang, Saumyadipta Pyne |
| Abstract |
Gradual or sudden transitions among different states as exhibited by cell populations in a biological sample under particular conditions or stimuli can be detected and profiled by flow cytometric time course data. Often such temporal profiles contain features due to transient states that present unique modeling challenges. These could range from asymmetric non-Gaussian distributions to outliers and tail subpopulations, which need to be modeled with precision and rigor. |
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 % |
|---|---|---|
| Unknown | 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 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Brazil | 1 | 6% |
| Unknown | 15 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 6 | 38% |
| Student > Ph. D. Student | 3 | 19% |
| Student > Bachelor | 2 | 13% |
| Professor | 1 | 6% |
| Professor > Associate Professor | 1 | 6% |
| Other | 0 | 0% |
| Unknown | 3 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 7 | 44% |
| Computer Science | 2 | 13% |
| Mathematics | 1 | 6% |
| Philosophy | 1 | 6% |
| Physics and Astronomy | 1 | 6% |
| Other | 1 | 6% |
| Unknown | 3 | 19% |
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 19 August 2013.
All research outputs
#18,343,746
of 22,716,996 outputs
Outputs from BMC Bioinformatics
#6,294
of 7,260 outputs
Outputs of similar age
#124,624
of 161,671 outputs
Outputs of similar age from BMC Bioinformatics
#77
of 93 outputs
Altmetric has tracked 22,716,996 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,260 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 5th percentile – i.e., 5% 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 161,671 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 9th percentile – i.e., 9% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 93 others from the same source and published within six weeks on either side of this one. This one is in the 5th percentile – i.e., 5% of its contemporaries scored the same or lower than it.