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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A reference profile-free deconvolution method to infer cancer cell-intrinsic subtypes and tumor-type-specific stromal profiles
|
|---|---|
| Published in |
Genome Medicine, February 2020
|
| DOI | 10.1186/s13073-020-0720-0 |
| Pubmed ID | |
| Authors |
Li Wang, Robert P. Sebra, John P. Sfakianos, Kimaada Allette, Wenhui Wang, Seungyeul Yoo, Nina Bhardwaj, Eric E. Schadt, Xin Yao, Matthew D. Galsky, Jun Zhu |
X Demographics
The data shown below were collected from the profiles of 19 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 | 11 | 58% |
| United Kingdom | 1 | 5% |
| Unknown | 7 | 37% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 15 | 79% |
| Scientists | 3 | 16% |
| Practitioners (doctors, other healthcare professionals) | 1 | 5% |
Mendeley readers
The data shown below were compiled from readership statistics for 69 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 69 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 12 | 17% |
| Researcher | 10 | 14% |
| Student > Ph. D. Student | 10 | 14% |
| Student > Bachelor | 7 | 10% |
| Professor > Associate Professor | 3 | 4% |
| Other | 5 | 7% |
| Unknown | 22 | 32% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 17 | 25% |
| Agricultural and Biological Sciences | 9 | 13% |
| Medicine and Dentistry | 6 | 9% |
| Computer Science | 5 | 7% |
| Engineering | 4 | 6% |
| Other | 6 | 9% |
| Unknown | 22 | 32% |
Attention Score in Context
This research output has an Altmetric Attention Score of 12. 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 February 2021.
All research outputs
#2,620,404
of 23,197,711 outputs
Outputs from Genome Medicine
#599
of 1,452 outputs
Outputs of similar age
#58,960
of 359,239 outputs
Outputs of similar age from Genome Medicine
#13
of 25 outputs
Altmetric has tracked 23,197,711 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,452 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 25.8. This one has gotten more attention than average, scoring higher than 58% 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 359,239 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 83% 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 is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.