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X Demographics
Mendeley readers
| Title |
Metabolic Syndrome Prediction Using Machine Learning Models with Genetic and Clinical Information from a Nonobese Healthy Population
|
|---|---|
| Published in |
Genomics & Informatics, December 2018
|
| DOI | 10.5808/gi.2018.16.4.e31 |
| Pubmed ID | |
| Authors |
Eun Kyung Choe, Hwanseok Rhee, Seungjae Lee, Eunsoon Shin, Seung-Won Oh, Jong-Eun Lee, Seung Ho Choi |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 34 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 9 | 26% |
| Student > Master | 4 | 12% |
| Researcher | 3 | 9% |
| Student > Doctoral Student | 1 | 3% |
| Professor | 1 | 3% |
| Other | 3 | 9% |
| Unknown | 13 | 38% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 6 | 18% |
| Computer Science | 5 | 15% |
| Mathematics | 2 | 6% |
| Engineering | 2 | 6% |
| Nursing and Health Professions | 2 | 6% |
| Other | 4 | 12% |
| Unknown | 13 | 38% |