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Mendeley readers
| Title |
HHMMiR: efficient de novo prediction of microRNAs using hierarchical hidden Markov models
|
|---|---|
| Published in |
BMC Bioinformatics, January 2009
|
| DOI | 10.1186/1471-2105-10-s1-s35 |
| Pubmed ID | |
| Authors |
Sabah Kadri, Veronica Hinman, Panayiotis V Benos |
Mendeley readers
The data shown below were compiled from readership statistics for 117 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 6 | 5% |
| Colombia | 2 | 2% |
| Germany | 2 | 2% |
| Brazil | 2 | 2% |
| Canada | 2 | 2% |
| Hong Kong | 1 | <1% |
| Sweden | 1 | <1% |
| China | 1 | <1% |
| France | 1 | <1% |
| Other | 2 | 2% |
| Unknown | 97 | 83% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 33 | 28% |
| Student > Master | 23 | 20% |
| Researcher | 22 | 19% |
| Professor > Associate Professor | 6 | 5% |
| Student > Bachelor | 4 | 3% |
| Other | 17 | 15% |
| Unknown | 12 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 51 | 44% |
| Computer Science | 18 | 15% |
| Biochemistry, Genetics and Molecular Biology | 18 | 15% |
| Engineering | 3 | 3% |
| Mathematics | 2 | 2% |
| Other | 8 | 7% |
| Unknown | 17 | 15% |