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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Exact score distribution computation for ontological similarity searches
|
|---|---|
| Published in |
BMC Bioinformatics, November 2011
|
| DOI | 10.1186/1471-2105-12-441 |
| Pubmed ID | |
| Authors |
Marcel H Schulz, Sebastian Köhler, Sebastian Bauer, Peter N Robinson |
| Abstract |
Semantic similarity searches in ontologies are an important component of many bioinformatic algorithms, e.g., finding functionally related proteins with the Gene Ontology or phenotypically similar diseases with the Human Phenotype Ontology (HPO). We have recently shown that the performance of semantic similarity searches can be improved by ranking results according to the probability of obtaining a given score at random rather than by the scores themselves. However, to date, there are no algorithms for computing the exact distribution of semantic similarity scores, which is necessary for computing the exact P-value of a given score. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 3 | 7% |
| United States | 2 | 5% |
| United Kingdom | 1 | 2% |
| Canada | 1 | 2% |
| Unknown | 37 | 84% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 16 | 36% |
| Student > Ph. D. Student | 9 | 20% |
| Student > Master | 4 | 9% |
| Other | 4 | 9% |
| Student > Doctoral Student | 2 | 5% |
| Other | 5 | 11% |
| Unknown | 4 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 14 | 32% |
| Computer Science | 11 | 25% |
| Biochemistry, Genetics and Molecular Biology | 5 | 11% |
| Medicine and Dentistry | 3 | 7% |
| Business, Management and Accounting | 1 | 2% |
| Other | 3 | 7% |
| Unknown | 7 | 16% |
Attention Score in Context
This research output has an Altmetric Attention Score of 15. 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 29 July 2014.
All research outputs
#2,058,778
of 22,656,971 outputs
Outputs from BMC Bioinformatics
#546
of 7,236 outputs
Outputs of similar age
#11,022
of 141,888 outputs
Outputs of similar age from BMC Bioinformatics
#13
of 118 outputs
Altmetric has tracked 22,656,971 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,236 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done particularly well, scoring higher than 92% of its peers.
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 141,888 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 118 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.