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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Diagnostic Evidence GAuge of Single cells (DEGAS): a flexible deep transfer learning framework for prioritizing cells in relation to disease
|
|---|---|
| Published in |
Genome Medicine, February 2022
|
| DOI | 10.1186/s13073-022-01012-2 |
| Pubmed ID | |
| Authors |
Travis S. Johnson, Christina Y. Yu, Zhi Huang, Siwen Xu, Tongxin Wang, Chuanpeng Dong, Wei Shao, Mohammad Abu Zaid, Xiaoqing Huang, Yijie Wang, Christopher Bartlett, Yan Zhang, Brian A. Walker, Yunlong Liu, Kun Huang, Jie Zhang |
X Demographics
The data shown below were collected from the profiles of 15 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 | 3 | 20% |
| United Kingdom | 2 | 13% |
| India | 1 | 7% |
| Italy | 1 | 7% |
| Unknown | 8 | 53% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 7 | 47% |
| Scientists | 7 | 47% |
| Practitioners (doctors, other healthcare professionals) | 1 | 7% |
Mendeley readers
The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 33 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 5 | 15% |
| Student > Master | 5 | 15% |
| Student > Bachelor | 3 | 9% |
| Unspecified | 2 | 6% |
| Researcher | 2 | 6% |
| Other | 2 | 6% |
| Unknown | 14 | 42% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 3 | 9% |
| Unspecified | 2 | 6% |
| Computer Science | 2 | 6% |
| Mathematics | 2 | 6% |
| Neuroscience | 2 | 6% |
| Other | 5 | 15% |
| Unknown | 17 | 52% |
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 12 February 2022.
All research outputs
#4,053,895
of 23,577,761 outputs
Outputs from Genome Medicine
#810
of 1,467 outputs
Outputs of similar age
#93,218
of 513,945 outputs
Outputs of similar age from Genome Medicine
#19
of 28 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 25.9. This one is in the 44th percentile – i.e., 44% of its peers scored the same or lower than it.
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 513,945 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 81% of its contemporaries.
We're also able to compare this research output to 28 others from the same source and published within six weeks on either side of this one. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.