| Title |
Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties
|
|---|---|
| Published in |
PLOS ONE, April 2013
|
| DOI | 10.1371/journal.pone.0061318 |
| Pubmed ID | |
| Authors |
Michael P. Menden, Francesco Iorio, Mathew Garnett, Ultan McDermott, Cyril H. Benes, Pedro J. Ballester, Julio Saez-Rodriguez |
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 % |
|---|---|---|
| United Kingdom | 3 | 25% |
| United States | 2 | 17% |
| Korea, Republic of | 1 | 8% |
| Canada | 1 | 8% |
| Spain | 1 | 8% |
| Germany | 1 | 8% |
| Switzerland | 1 | 8% |
| Unknown | 2 | 17% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 7 | 58% |
| Members of the public | 5 | 42% |
Mendeley readers
The data shown below were compiled from readership statistics for 619 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 10 | 2% |
| Germany | 4 | <1% |
| United Kingdom | 3 | <1% |
| India | 2 | <1% |
| Denmark | 2 | <1% |
| Austria | 1 | <1% |
| Canada | 1 | <1% |
| Italy | 1 | <1% |
| Spain | 1 | <1% |
| Other | 1 | <1% |
| Unknown | 593 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 156 | 25% |
| Researcher | 92 | 15% |
| Student > Master | 76 | 12% |
| Student > Bachelor | 59 | 10% |
| Student > Postgraduate | 28 | 5% |
| Other | 91 | 15% |
| Unknown | 117 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 124 | 20% |
| Computer Science | 105 | 17% |
| Biochemistry, Genetics and Molecular Biology | 93 | 15% |
| Engineering | 37 | 6% |
| Medicine and Dentistry | 29 | 5% |
| Other | 103 | 17% |
| Unknown | 128 | 21% |