| Title |
Assessing Computational Methods for Transcription Factor Target Gene Identification Based on ChIP-seq Data
|
|---|---|
| Published in |
PLoS Computational Biology, November 2013
|
| DOI | 10.1371/journal.pcbi.1003342 |
| Pubmed ID | |
| Authors |
Weronika Sikora-Wohlfeld, Marit Ackermann, Eleni G. Christodoulou, Kalaimathy Singaravelu, Andreas Beyer |
X Demographics
The data shown below were collected from the profiles of 15 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 States | 4 | 27% |
| Germany | 1 | 7% |
| New Zealand | 1 | 7% |
| Norway | 1 | 7% |
| Australia | 1 | 7% |
| China | 1 | 7% |
| Netherlands | 1 | 7% |
| India | 1 | 7% |
| Unknown | 4 | 27% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 8 | 53% |
| Members of the public | 7 | 47% |
Mendeley readers
The data shown below were compiled from readership statistics for 171 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 8 | 5% |
| United Kingdom | 5 | 3% |
| Germany | 2 | 1% |
| Australia | 1 | <1% |
| Singapore | 1 | <1% |
| Brazil | 1 | <1% |
| Greece | 1 | <1% |
| Korea, Republic of | 1 | <1% |
| Unknown | 151 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 47 | 27% |
| Researcher | 40 | 23% |
| Student > Master | 23 | 13% |
| Professor > Associate Professor | 11 | 6% |
| Student > Doctoral Student | 10 | 6% |
| Other | 28 | 16% |
| Unknown | 12 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 82 | 48% |
| Biochemistry, Genetics and Molecular Biology | 42 | 25% |
| Computer Science | 17 | 10% |
| Medicine and Dentistry | 5 | 3% |
| Neuroscience | 4 | 2% |
| Other | 6 | 4% |
| Unknown | 15 | 9% |