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Mendeley readers
| Title |
Letter to the editor: a response to Ming’s study on machine learning techniques for personalized breast cancer risk prediction
|
|---|---|
| Published in |
Breast Cancer Research, February 2020
|
| DOI | 10.1186/s13058-020-1255-4 |
| Pubmed ID | |
| Authors |
Daniele Giardiello, Antonis C. Antoniou, Luigi Mariani, Douglas F. Easton, Ewout W. Steyerberg |
Mendeley readers
The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 16 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 3 | 19% |
| Unspecified | 1 | 6% |
| Lecturer | 1 | 6% |
| Student > Ph. D. Student | 1 | 6% |
| Student > Bachelor | 1 | 6% |
| Other | 2 | 13% |
| Unknown | 7 | 44% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 2 | 13% |
| Nursing and Health Professions | 2 | 13% |
| Arts and Humanities | 1 | 6% |
| Unspecified | 1 | 6% |
| Biochemistry, Genetics and Molecular Biology | 1 | 6% |
| Other | 2 | 13% |
| Unknown | 7 | 44% |