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X Demographics
Mendeley readers
Attention Score in Context
| Title |
AllerTOP - a server for in silico prediction of allergens
|
|---|---|
| Published in |
BMC Bioinformatics, April 2013
|
| DOI | 10.1186/1471-2105-14-s6-s4 |
| Pubmed ID | |
| Authors |
Ivan Dimitrov, Darren R Flower, Irini Doytchinova |
| Abstract |
Allergy is a form of hypersensitivity to normally innocuous substances, such as dust, pollen, foods or drugs. Allergens are small antigens that commonly provoke an IgE antibody response. There are two types of bioinformatics-based allergen prediction. The first approach follows FAO/WHO Codex alimentarius guidelines and searches for sequence similarity. The second approach is based on identifying conserved allergenicity-related linear motifs. Both approaches assume that allergenicity is a linearly coded property. In the present study, we applied ACC pre-processing to sets of known allergens, developing alignment-independent models for allergen recognition based on the main chemical properties of amino acid sequences. |
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 % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 213 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 213 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 28 | 13% |
| Student > Ph. D. Student | 19 | 9% |
| Researcher | 16 | 8% |
| Student > Master | 16 | 8% |
| Professor > Associate Professor | 6 | 3% |
| Other | 21 | 10% |
| Unknown | 107 | 50% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 44 | 21% |
| Agricultural and Biological Sciences | 12 | 6% |
| Immunology and Microbiology | 12 | 6% |
| Medicine and Dentistry | 6 | 3% |
| Engineering | 6 | 3% |
| Other | 15 | 7% |
| Unknown | 118 | 55% |
Attention Score in Context
This research output has an Altmetric Attention Score of 11. 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 15 June 2023.
All research outputs
#3,014,069
of 23,867,274 outputs
Outputs from BMC Bioinformatics
#995
of 7,478 outputs
Outputs of similar age
#25,230
of 200,065 outputs
Outputs of similar age from BMC Bioinformatics
#23
of 124 outputs
Altmetric has tracked 23,867,274 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,478 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done well, scoring higher than 86% 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 200,065 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 124 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.