The data shown below were collected from the profiles of 20 X users who shared this research output. Click here to find out more about how the information was compiled.
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X Demographics
Mendeley readers
Attention Score in Context
Title |
Using Machine Learning to Predict Obesity Based on Genome-Wide and Epigenome-Wide Gene–Gene and Gene–Diet Interactions
|
---|---|
Published in |
Frontiers in Genetics, January 2022
|
DOI | 10.3389/fgene.2021.783845 |
Pubmed ID | |
Authors |
Yu-Chi Lee, Jacob J. Christensen, Laurence D. Parnell, Caren E. Smith, Jonathan Shao, Nicola M. McKeown, José M. Ordovás, Chao-Qiang Lai |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 5 | 25% |
Canada | 1 | 5% |
Malaysia | 1 | 5% |
Italy | 1 | 5% |
Norway | 1 | 5% |
Unknown | 11 | 55% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 11 | 55% |
Scientists | 7 | 35% |
Practitioners (doctors, other healthcare professionals) | 2 | 10% |
Mendeley readers
The data shown below were compiled from readership statistics for 82 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 82 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 7 | 9% |
Student > Master | 6 | 7% |
Student > Bachelor | 6 | 7% |
Professor | 5 | 6% |
Researcher | 4 | 5% |
Other | 13 | 16% |
Unknown | 41 | 50% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 7 | 9% |
Agricultural and Biological Sciences | 6 | 7% |
Medicine and Dentistry | 5 | 6% |
Engineering | 5 | 6% |
Computer Science | 4 | 5% |
Other | 10 | 12% |
Unknown | 45 | 55% |
Attention Score in Context
This research output has an Altmetric Attention Score of 11. 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 01 July 2022.
All research outputs
#3,323,349
of 26,078,244 outputs
Outputs from Frontiers in Genetics
#857
of 13,848 outputs
Outputs of similar age
#78,800
of 528,467 outputs
Outputs of similar age from Frontiers in Genetics
#30
of 810 outputs
Altmetric has tracked 26,078,244 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 13,848 research outputs from this source. They receive a mean Attention Score of 3.9. This one has done particularly well, scoring higher than 93% 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 528,467 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 810 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 96% of its contemporaries.