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Improved Method for Linear B-Cell Epitope Prediction Using Antigen’s Primary Sequence

Overview of attention for article published in PLOS ONE, May 2013
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (91st percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

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2 X users
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13 patents
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3 Wikipedia pages
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1 Redditor

Citations

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295 Dimensions

Readers on

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279 Mendeley
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2 CiteULike
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Title
Improved Method for Linear B-Cell Epitope Prediction Using Antigen’s Primary Sequence
Published in
PLOS ONE, May 2013
DOI 10.1371/journal.pone.0062216
Pubmed ID
Authors

Harinder Singh, Hifzur Rahman Ansari, Gajendra P. S. Raghava

Abstract

One of the major challenges in designing a peptide-based vaccine is the identification of antigenic regions in an antigen that can stimulate B-cell's response, also called B-cell epitopes. In the past, several methods have been developed for the prediction of conformational and linear (or continuous) B-cell epitopes. However, the existing methods for predicting linear B-cell epitopes are far from perfection. In this study, an attempt has been made to develop an improved method for predicting linear B-cell epitopes. We have retrieved experimentally validated B-cell epitopes as well as non B-cell epitopes from Immune Epitope Database and derived two types of datasets called Lbtope_Variable and Lbtope_Fixed length datasets. The Lbtope_Variable dataset contains 14876 B-cell epitope and 23321 non-epitopes of variable length where as Lbtope_Fixed length dataset contains 12063 B-cell epitopes and 20589 non-epitopes of fixed length. We also evaluated the performance of models on above datasets after removing highly identical peptides from the datasets. In addition, we have derived third dataset Lbtope_Confirm having 1042 epitopes and 1795 non-epitopes where each epitope or non-epitope has been experimentally validated in at least two studies. A number of models have been developed to discriminate epitopes and non-epitopes using different machine-learning techniques like Support Vector Machine, and K-Nearest Neighbor. We achieved accuracy from ∼54% to 86% using diverse s features like binary profile, dipeptide composition, AAP (amino acid pair) profile. In this study, for the first time experimentally validated non B-cell epitopes have been used for developing method for predicting linear B-cell epitopes. In previous studies, random peptides have been used as non B-cell epitopes. In order to provide service to scientific community, a web server LBtope has been developed for predicting and designing B-cell epitopes (http://crdd.osdd.net/raghava/lbtope/).

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 279 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
India 2 <1%
Unknown 277 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 59 21%
Student > Bachelor 39 14%
Student > Master 34 12%
Researcher 27 10%
Student > Doctoral Student 13 5%
Other 38 14%
Unknown 69 25%
Readers by discipline Count As %
Agricultural and Biological Sciences 69 25%
Biochemistry, Genetics and Molecular Biology 64 23%
Immunology and Microbiology 14 5%
Medicine and Dentistry 13 5%
Computer Science 12 4%
Other 27 10%
Unknown 80 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 17. 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 08 August 2023.
All research outputs
#2,171,573
of 25,782,917 outputs
Outputs from PLOS ONE
#26,303
of 224,768 outputs
Outputs of similar age
#17,682
of 206,212 outputs
Outputs of similar age from PLOS ONE
#593
of 4,953 outputs
Altmetric has tracked 25,782,917 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 224,768 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.8. This one has done well, scoring higher than 88% 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 206,212 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 91% of its contemporaries.
We're also able to compare this research output to 4,953 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.