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Jenner-predict server: prediction of protein vaccine candidates (PVCs) in bacteria based on host-pathogen interactions

Overview of attention for article published in BMC Bioinformatics, July 2013
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (85th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

blogs
1 blog
twitter
4 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
46 Dimensions

Readers on

mendeley
82 Mendeley

What is this page?

Summary Blogs Twitter Facebook Dimensions citations
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Title
Jenner-predict server: prediction of protein vaccine candidates (PVCs) in bacteria based on host-pathogen interactions
Published in
BMC Bioinformatics, July 2013
DOI 10.1186/1471-2105-14-211
Pubmed ID
23815072
Authors

Varun Jaiswal, Sree Krishna Chanumolu, Ankit Gupta, Rajinder S Chauhan, Chittaranjan Rout

Abstract

Subunit vaccines based on recombinant proteins have been effective in preventing infectious diseases and are expected to meet the demands of future vaccine development. Computational approach, especially reverse vaccinology (RV) method has enormous potential for identification of protein vaccine candidates (PVCs) from a proteome. The existing protective antigen prediction software and web servers have low prediction accuracy leading to limited applications for vaccine development. Besides machine learning techniques, those software and web servers have considered only protein's adhesin-likeliness as criterion for identification of PVCs. Several non-adhesin functional classes of proteins involved in host-pathogen interactions and pathogenesis are known to provide protection against bacterial infections. Therefore, knowledge of bacterial pathogenesis has potential to identify PVCs.

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Twitter Demographics

The data shown below were collected from the profiles of 4 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 82 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 82 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 14 17%
Student > Ph. D. Student 12 15%
Student > Bachelor 12 15%
Researcher 9 11%
Other 8 10%
Other 17 21%
Unknown 10 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 27 33%
Biochemistry, Genetics and Molecular Biology 20 24%
Immunology and Microbiology 7 9%
Engineering 5 6%
Medicine and Dentistry 3 4%
Other 5 6%
Unknown 15 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 06 November 2017.
All research outputs
#2,298,241
of 17,360,236 outputs
Outputs from BMC Bioinformatics
#914
of 6,151 outputs
Outputs of similar age
#22,685
of 163,667 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 5 outputs
Altmetric has tracked 17,360,236 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,151 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one has done well, scoring higher than 84% 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 163,667 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 85% of its contemporaries.
We're also able to compare this research output to 5 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

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