| Title |
Predicting Positive p53 Cancer Rescue Regions Using Most Informative Positive (MIP) Active Learning
|
|---|---|
| Published in |
PLoS Computational Biology, September 2009
|
| DOI | 10.1371/journal.pcbi.1000498 |
| Pubmed ID | |
| Authors |
Samuel A. Danziger, Roberta Baronio, Lydia Ho, Linda Hall, Kirsty Salmon, G. Wesley Hatfield, Peter Kaiser, Richard H. Lathrop |
Mendeley readers
The data shown below were compiled from readership statistics for 82 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 6 | 7% |
| Netherlands | 1 | 1% |
| Canada | 1 | 1% |
| Australia | 1 | 1% |
| Unknown | 73 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 25 | 30% |
| Researcher | 17 | 21% |
| Student > Master | 6 | 7% |
| Student > Bachelor | 6 | 7% |
| Student > Postgraduate | 5 | 6% |
| Other | 10 | 12% |
| Unknown | 13 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 21 | 26% |
| Biochemistry, Genetics and Molecular Biology | 14 | 17% |
| Computer Science | 13 | 16% |
| Engineering | 5 | 6% |
| Chemistry | 3 | 4% |
| Other | 8 | 10% |
| Unknown | 18 | 22% |