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Staged heterogeneity learning to identify conformational B-cell epitopes from antigen sequences

Overview of attention for article published in BMC Genomics, March 2017
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Title
Staged heterogeneity learning to identify conformational B-cell epitopes from antigen sequences
Published in
BMC Genomics, March 2017
DOI 10.1186/s12864-017-3493-0
Pubmed ID
Authors

Jing Ren, Jiangning Song, John Ellis, Jinyan Li

Abstract

The broad heterogeneity of antigen-antibody interactions brings tremendous challenges to the design of a widely applicable learning algorithm to identify conformational B-cell epitopes. Besides the intrinsic heterogeneity introduced by diverse species, extra heterogeneity can also be introduced by various data sources, adding another layer of complexity and further confounding the research. This work proposed a staged heterogeneity learning method, which learns both characteristics and heterogeneity of data in a phased manner. The method was applied to identify antigenic residues of heterogenous conformational B-cell epitopes based on antigen sequences. In the first stage, the model learns the general epitope patterns of each kind of propensity from a large data set containing computationally defined epitopes. In the second stage, the model learns the heterogenous complementarity of these propensities from a relatively small guided data set containing experimentally determined epitopes. Moreover, we designed an algorithm to cluster the predicted individual antigenic residues into conformational B-cell epitopes so as to provide strong potential for real-world applications, such as vaccine development. With heterogeneity well learnt, the transferability of the prediction model was remarkably improved to handle new data with a high level of heterogeneity. The model has been tested on two data sets with experimentally determined epitopes, and on a data set with computationally defined epitopes. This proposed sequence-based method achieved outstanding performance - about twice that of existing methods, including the sequence-based predictor CBTOPE and three other structure-based predictors. The proposed method uses only antigen sequence information, and thus has much broader applications.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 15 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 3 20%
Student > Ph. D. Student 3 20%
Student > Master 2 13%
Other 1 7%
Professor 1 7%
Other 3 20%
Unknown 2 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 3 20%
Immunology and Microbiology 3 20%
Biochemistry, Genetics and Molecular Biology 1 7%
Nursing and Health Professions 1 7%
Unspecified 1 7%
Other 4 27%
Unknown 2 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 01 April 2017.
All research outputs
#15,702,774
of 23,335,153 outputs
Outputs from BMC Genomics
#6,774
of 10,744 outputs
Outputs of similar age
#195,547
of 308,803 outputs
Outputs of similar age from BMC Genomics
#128
of 201 outputs
Altmetric has tracked 23,335,153 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,744 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 28th percentile – i.e., 28% of its peers scored the same or lower than it.
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We're also able to compare this research output to 201 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.