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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A Machine Learned Classifier That Uses Gene Expression Data to Accurately Predict Estrogen Receptor Status
|
|---|---|
| Published in |
PLOS ONE, December 2013
|
| DOI | 10.1371/journal.pone.0082144 |
| Pubmed ID | |
| Authors |
Meysam Bastani, Larissa Vos, Nasimeh Asgarian, Jean Deschenes, Kathryn Graham, John Mackey, Russell Greiner |
| Abstract |
Selecting the appropriate treatment for breast cancer requires accurately determining the estrogen receptor (ER) status of the tumor. However, the standard for determining this status, immunohistochemical analysis of formalin-fixed paraffin embedded samples, suffers from numerous technical and reproducibility issues. Assessment of ER-status based on RNA expression can provide more objective, quantitative and reproducible test results. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 2% |
| United States | 1 | 2% |
| Unknown | 58 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 10 | 17% |
| Other | 6 | 10% |
| Student > Ph. D. Student | 6 | 10% |
| Student > Bachelor | 5 | 8% |
| Student > Master | 4 | 7% |
| Other | 13 | 22% |
| Unknown | 16 | 27% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 15 | 25% |
| Computer Science | 8 | 13% |
| Agricultural and Biological Sciences | 6 | 10% |
| Engineering | 6 | 10% |
| Biochemistry, Genetics and Molecular Biology | 5 | 8% |
| Other | 3 | 5% |
| Unknown | 17 | 28% |
Attention Score in Context
This research output has an Altmetric Attention Score of 18. 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 December 2013.
All research outputs
#1,741,259
of 22,733,113 outputs
Outputs from PLOS ONE
#22,434
of 194,037 outputs
Outputs of similar age
#21,112
of 307,218 outputs
Outputs of similar age from PLOS ONE
#624
of 5,065 outputs
Altmetric has tracked 22,733,113 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 194,037 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.1. This one has done well, scoring higher than 88% 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 307,218 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 5,065 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.