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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
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Published in |
BMC Biology, April 2021
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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 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 44 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 6 | 14% |
Student > Master | 5 | 11% |
Researcher | 5 | 11% |
Student > Bachelor | 2 | 5% |
Other | 2 | 5% |
Other | 5 | 11% |
Unknown | 19 | 43% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 5 | 11% |
Computer Science | 5 | 11% |
Medicine and Dentistry | 4 | 9% |
Agricultural and Biological Sciences | 3 | 7% |
Pharmacology, Toxicology and Pharmaceutical Science | 2 | 5% |
Other | 5 | 11% |
Unknown | 20 | 45% |