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X Demographics
Mendeley readers
Attention Score in Context
| Title |
NightShift: NMR shift inference by general hybrid model training - a framework for NMR chemical shift prediction
|
|---|---|
| Published in |
BMC Bioinformatics, March 2013
|
| DOI | 10.1186/1471-2105-14-98 |
| Pubmed ID | |
| Authors |
Anna Katharina Dehof, Simon Loew, Hans-Peter Lenhof, Andreas Hildebrandt |
| Abstract |
NMR chemical shift prediction plays an important role in various applications in computational biology. Among others, structure determination, structure optimization, and the scoring of docking results can profit from efficient and accurate chemical shift estimation from a three-dimensional model.A variety of NMR chemical shift prediction approaches have been presented in the past, but nearly all of these rely on laborious manual data set preparation and the training itself is not automatized, making retraining the model, e.g., if new data is made available, or testing new models a time-consuming manual chore. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Norway | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Practitioners (doctors, other healthcare professionals) | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 3% |
| Unknown | 28 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 8 | 28% |
| Student > Ph. D. Student | 6 | 21% |
| Professor | 4 | 14% |
| Professor > Associate Professor | 3 | 10% |
| Student > Bachelor | 2 | 7% |
| Other | 5 | 17% |
| Unknown | 1 | 3% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 8 | 28% |
| Chemistry | 6 | 21% |
| Agricultural and Biological Sciences | 4 | 14% |
| Engineering | 2 | 7% |
| Medicine and Dentistry | 2 | 7% |
| Other | 3 | 10% |
| Unknown | 4 | 14% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 March 2013.
All research outputs
#16,099,609
of 23,881,329 outputs
Outputs from BMC Bioinformatics
#5,488
of 7,454 outputs
Outputs of similar age
#126,334
of 198,863 outputs
Outputs of similar age from BMC Bioinformatics
#111
of 147 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,454 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 18th percentile – i.e., 18% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 198,863 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 147 others from the same source and published within six weeks on either side of this one. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.