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Mendeley readers
| Title |
Predicting conserved protein motifs with Sub-HMMs
|
|---|---|
| Published in |
BMC Bioinformatics, April 2010
|
| DOI | 10.1186/1471-2105-11-205 |
| Pubmed ID | |
| Authors |
Kevin Horan, Christian R Shelton, Thomas Girke |
| Abstract |
Profile HMMs (hidden Markov models) provide effective methods for modeling the conserved regions of protein families. A limitation of the resulting domain models is the difficulty to pinpoint their much shorter functional sub-features, such as catalytically relevant sequence motifs in enzymes or ligand binding signatures of receptor proteins. |
Mendeley readers
The data shown below were compiled from readership statistics for 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 5% |
| United Kingdom | 1 | 3% |
| Germany | 1 | 3% |
| Denmark | 1 | 3% |
| Argentina | 1 | 3% |
| Unknown | 33 | 85% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 11 | 28% |
| Researcher | 9 | 23% |
| Student > Master | 5 | 13% |
| Student > Bachelor | 4 | 10% |
| Student > Doctoral Student | 2 | 5% |
| Other | 4 | 10% |
| Unknown | 4 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 23 | 59% |
| Biochemistry, Genetics and Molecular Biology | 7 | 18% |
| Environmental Science | 1 | 3% |
| Mathematics | 1 | 3% |
| Computer Science | 1 | 3% |
| Other | 2 | 5% |
| Unknown | 4 | 10% |