You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
| Title |
A deep learning image-based intrinsic molecular subtype classifier of breast tumors reveals tumor heterogeneity that may affect survival
|
|---|---|
| Published in |
Breast Cancer Research, January 2020
|
| DOI | 10.1186/s13058-020-1248-3 |
| Pubmed ID | |
| Authors |
Mustafa I. Jaber, Bing Song, Clive Taylor, Charles J. Vaske, Stephen C. Benz, Shahrooz Rabizadeh, Patrick Soon-Shiong, Christopher W. Szeto |
X Demographics
The data shown below were collected from the profiles of 13 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 | 5 | 38% |
| Spain | 1 | 8% |
| Italy | 1 | 8% |
| Comoros | 1 | 8% |
| France | 1 | 8% |
| India | 1 | 8% |
| Unknown | 3 | 23% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 9 | 69% |
| Scientists | 4 | 31% |
Mendeley readers
The data shown below were compiled from readership statistics for 125 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 125 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 14 | 11% |
| Student > Ph. D. Student | 13 | 10% |
| Student > Master | 12 | 10% |
| Student > Bachelor | 9 | 7% |
| Unspecified | 8 | 6% |
| Other | 28 | 22% |
| Unknown | 41 | 33% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 19 | 15% |
| Computer Science | 16 | 13% |
| Biochemistry, Genetics and Molecular Biology | 12 | 10% |
| Engineering | 11 | 9% |
| Unspecified | 8 | 6% |
| Other | 18 | 14% |
| Unknown | 41 | 33% |
Attention Score in Context
This research output has an Altmetric Attention Score of 8. 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 17 April 2020.
All research outputs
#4,687,827
of 25,387,668 outputs
Outputs from Breast Cancer Research
#541
of 2,054 outputs
Outputs of similar age
#104,580
of 471,048 outputs
Outputs of similar age from Breast Cancer Research
#9
of 25 outputs
Altmetric has tracked 25,387,668 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,054 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.2. This one has gotten more attention than average, scoring higher than 73% 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 471,048 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 77% of its contemporaries.
We're also able to compare this research output to 25 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.