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AllerTOP - a server for in silico prediction of allergens

Overview of attention for article published in BMC Bioinformatics, April 2013
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  • Average Attention Score compared to outputs of the same age

Mentioned by

twitter
2 tweeters

Citations

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246 Dimensions

Readers on

mendeley
143 Mendeley
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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.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 143 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 143 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 29 20%
Researcher 15 10%
Student > Ph. D. Student 13 9%
Student > Master 11 8%
Professor 5 3%
Other 17 12%
Unknown 53 37%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 40 28%
Agricultural and Biological Sciences 11 8%
Immunology and Microbiology 11 8%
Medicine and Dentistry 5 3%
Engineering 5 3%
Other 10 7%
Unknown 61 43%

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 14 June 2013.
All research outputs
#13,335,184
of 21,346,377 outputs
Outputs from BMC Bioinformatics
#4,496
of 6,923 outputs
Outputs of similar age
#98,387
of 175,960 outputs
Outputs of similar age from BMC Bioinformatics
#22
of 33 outputs
Altmetric has tracked 21,346,377 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,923 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 30th percentile – i.e., 30% 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 175,960 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 33 others from the same source and published within six weeks on either side of this one. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.