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

Clicks: 256
ID: 100975
2017
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #78 of 121 articles by views in BMC genomics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 121 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
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.
Reference Key
ren2017stagedbmc Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ren, Jing;Song, Jiangning;Ellis, John;Li, Jinyan;
Journal BMC genomics
Year 2017
DOI
10.1186/s12864-017-3493-0
URL
Keywords
epitope prediction b-cell epitope conformational epitope sequence-based staged heterogeneity learning

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.