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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Decomposing Phenotype Descriptions for the Human Skeletal Phenome
|
|---|---|
| Published in |
Biomedical Informatics Insights, February 2013
|
| DOI | 10.4137/bii.s10729 |
| Pubmed ID | |
| Authors |
Tudor Groza, Jane Hunter, Andreas Zankl |
| Abstract |
Over the course of the last few years there has been a significant amount of research performed on ontology-based formalization of phenotype descriptions. The intrinsic value and knowledge captured within such descriptions can only be expressed by taking advantage of their inner structure that implicitly combines qualities and anatomical entities. We present a meta-model (the Phenotype Fragment Ontology) and a processing pipeline that enable together the automatic decomposition and conceptualization of phenotype descriptions for the human skeletal phenome. We use this approach to showcase the usefulness of the generic concept of phenotype decomposition by performing an experimental study on all skeletal phenotype concepts defined in the Human Phenotype Ontology. |
X Demographics
The data shown below were collected from the profiles of 2 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 Kingdom | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 14 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 3 | 21% |
| Other | 2 | 14% |
| Researcher | 2 | 14% |
| Student > Master | 2 | 14% |
| Professor > Associate Professor | 2 | 14% |
| Other | 2 | 14% |
| Unknown | 1 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 3 | 21% |
| Agricultural and Biological Sciences | 3 | 21% |
| Computer Science | 3 | 21% |
| Social Sciences | 1 | 7% |
| Medicine and Dentistry | 1 | 7% |
| Other | 0 | 0% |
| Unknown | 3 | 21% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 18 March 2013.
All research outputs
#16,721,208
of 25,373,627 outputs
Outputs from Biomedical Informatics Insights
#28
of 54 outputs
Outputs of similar age
#188,045
of 291,540 outputs
Outputs of similar age from Biomedical Informatics Insights
#1
of 1 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 54 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.1. This one is in the 42nd percentile – i.e., 42% 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 291,540 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them