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X Demographics
Mendeley readers
| Title |
A cell-to-patient machine learning transfer approach uncovers novel basal-like breast cancer prognostic markers amongst alternative splice variants
|
|---|---|
| Published in |
BMC Biology, April 2021
|
| DOI | 10.1186/s12915-021-01002-7 |
| Pubmed ID | |
| Authors |
Jean-Philippe Villemin, Claudio Lorenzi, Marie-Sarah Cabrillac, Andrew Oldfield, William Ritchie, Reini F. Luco |
X Demographics
The data shown below were collected from the profiles of 12 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 2 | 17% |
| United Kingdom | 1 | 8% |
| Australia | 1 | 8% |
| Unknown | 8 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 7 | 58% |
| Scientists | 3 | 25% |
| Science communicators (journalists, bloggers, editors) | 2 | 17% |
Mendeley readers
The data shown below were compiled from readership statistics for 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 45 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 13% |
| Researcher | 5 | 11% |
| Student > Master | 4 | 9% |
| Student > Bachelor | 2 | 4% |
| Other | 2 | 4% |
| Other | 5 | 11% |
| Unknown | 21 | 47% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 6 | 13% |
| Medicine and Dentistry | 4 | 9% |
| Computer Science | 4 | 9% |
| Agricultural and Biological Sciences | 3 | 7% |
| Pharmacology, Toxicology and Pharmaceutical Science | 2 | 4% |
| Other | 4 | 9% |
| Unknown | 22 | 49% |