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| Title |
A Genome-Wide Association Study and Machine-Learning Algorithm Analysis on the Prediction of Facial Phenotypes by Genotypes in Korean Women
|
|---|---|
| Published in |
Clinical, Cosmetic and Investigational Dermatology, March 2022
|
| DOI | 10.2147/ccid.s339547 |
| Pubmed ID | |
| Authors |
Hye-Young Yoo, Ki-Chan Lee, Ji-Eun Woo, Sung-Ha Park, Sunghoon Lee, Joungsu Joo, Jin-Sik Bae, Hyuk-Jung Kwon, Byoung-Jun Park |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 29% |
| India | 1 | 14% |
| Unknown | 4 | 57% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 7 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 10 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Unspecified | 1 | 10% |
| Librarian | 1 | 10% |
| Student > Ph. D. Student | 1 | 10% |
| Researcher | 1 | 10% |
| Student > Master | 1 | 10% |
| Other | 0 | 0% |
| Unknown | 5 | 50% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 2 | 20% |
| Unspecified | 1 | 10% |
| Nursing and Health Professions | 1 | 10% |
| Unknown | 6 | 60% |
Attention Score in Context
This research output has an Altmetric Attention Score of 13. 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 25 March 2022.
All research outputs
#2,879,792
of 26,557,556 outputs
Outputs from Clinical, Cosmetic and Investigational Dermatology
#207
of 952 outputs
Outputs of similar age
#65,491
of 457,780 outputs
Outputs of similar age from Clinical, Cosmetic and Investigational Dermatology
#7
of 35 outputs
Altmetric has tracked 26,557,556 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 952 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 22.3. This one has done well, scoring higher than 78% 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 457,780 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 85% of its contemporaries.
We're also able to compare this research output to 35 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.