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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Single-cell analyses to tailor treatments
|
|---|---|
| Published in |
Science Translational Medicine, September 2017
|
| DOI | 10.1126/scitranslmed.aan4730 |
| Pubmed ID | |
| Authors |
Alex K Shalek, Mikael Benson |
| Abstract |
Single-cell RNA-seq could play a key role in personalized medicine by facilitating characterization of cells, pathways, and genes associated with human diseases such as cancer. |
X Demographics
The data shown below were collected from the profiles of 97 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 | 34 | 35% |
| United Kingdom | 9 | 9% |
| France | 7 | 7% |
| Germany | 5 | 5% |
| India | 3 | 3% |
| Australia | 3 | 3% |
| Spain | 2 | 2% |
| Canada | 2 | 2% |
| Korea, Republic of | 1 | 1% |
| Other | 9 | 9% |
| Unknown | 22 | 23% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 48 | 49% |
| Scientists | 47 | 48% |
| Practitioners (doctors, other healthcare professionals) | 2 | 2% |
Mendeley readers
The data shown below were compiled from readership statistics for 326 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 326 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 72 | 22% |
| Student > Ph. D. Student | 63 | 19% |
| Student > Master | 30 | 9% |
| Other | 16 | 5% |
| Student > Postgraduate | 16 | 5% |
| Other | 53 | 16% |
| Unknown | 76 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 82 | 25% |
| Agricultural and Biological Sciences | 54 | 17% |
| Medicine and Dentistry | 25 | 8% |
| Engineering | 18 | 6% |
| Immunology and Microbiology | 17 | 5% |
| Other | 48 | 15% |
| Unknown | 82 | 25% |
Attention Score in Context
This research output has an Altmetric Attention Score of 67. 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 November 2021.
All research outputs
#628,099
of 25,284,710 outputs
Outputs from Science Translational Medicine
#1,542
of 5,415 outputs
Outputs of similar age
#12,975
of 324,369 outputs
Outputs of similar age from Science Translational Medicine
#40
of 111 outputs
Altmetric has tracked 25,284,710 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,415 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 86.5. This one has gotten more attention than average, scoring higher than 71% 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 324,369 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 96% of its contemporaries.
We're also able to compare this research output to 111 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 64% of its contemporaries.