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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A dynamic single cell-based framework for digital twins to prioritize disease genes and drug targets
|
|---|---|
| Published in |
Genome Medicine, May 2022
|
| DOI | 10.1186/s13073-022-01048-4 |
| Pubmed ID | |
| Authors |
Xinxiu Li, Eun Jung Lee, Sandra Lilja, Joseph Loscalzo, Samuel Schäfer, Martin Smelik, Maria Regina Strobl, Oleg Sysoev, Hui Wang, Huan Zhang, Yelin Zhao, Danuta R. Gawel, Barbara Bohle, Mikael Benson |
X Demographics
The data shown below were collected from the profiles of 24 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 Kingdom | 4 | 17% |
| United States | 3 | 13% |
| Uganda | 1 | 4% |
| Spain | 1 | 4% |
| Korea, Republic of | 1 | 4% |
| Austria | 1 | 4% |
| Sweden | 1 | 4% |
| Italy | 1 | 4% |
| Unknown | 11 | 46% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 13 | 54% |
| Scientists | 9 | 38% |
| Practitioners (doctors, other healthcare professionals) | 1 | 4% |
| Science communicators (journalists, bloggers, editors) | 1 | 4% |
Mendeley readers
The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 47 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 8 | 17% |
| Unspecified | 4 | 9% |
| Student > Ph. D. Student | 4 | 9% |
| Student > Master | 3 | 6% |
| Student > Doctoral Student | 2 | 4% |
| Other | 8 | 17% |
| Unknown | 18 | 38% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 7 | 15% |
| Unspecified | 4 | 9% |
| Engineering | 3 | 6% |
| Nursing and Health Professions | 2 | 4% |
| Agricultural and Biological Sciences | 2 | 4% |
| Other | 9 | 19% |
| Unknown | 20 | 43% |
Attention Score in Context
This research output has an Altmetric Attention Score of 135. 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 02 October 2022.
All research outputs
#311,744
of 25,630,321 outputs
Outputs from Genome Medicine
#50
of 1,605 outputs
Outputs of similar age
#8,662
of 446,626 outputs
Outputs of similar age from Genome Medicine
#2
of 34 outputs
Altmetric has tracked 25,630,321 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,605 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.6. This one has done particularly well, scoring higher than 96% 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 446,626 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 98% of its contemporaries.
We're also able to compare this research output to 34 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 94% of its contemporaries.