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10.1186/s12864-017-3493-0 http://scihub22266oqcxt.onion/10.1186/s12864-017-3493-0 C5374683!5374683!28361709     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=28361709&cmd=llinks): Failed to open stream: HTTP request failed! 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Staged heterogeneity learning to identify conformational B-cell epitopes from antigen sequences |pdf=|usr=}}{{28361709}} gab.com Text Staged heterogeneity learning to identify conformational B-cell epitopes from antigen sequences JO: BMC Genomics http://www.kidney.de/pget.php?&tw=1&pmid=28361709 #FOAvip Background: 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. Results: 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. Conclusions: The proposed method uses only antigen sequence information, and thus has much broader applications. Twit Text FOAVip Staged heterogeneity learning to identify conformational B-cell epitopes from antigen sequences ????BMC Genomics http://www.kidney.de/pget.php?&tw=1&pmid=28361709 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28361709 #FOAvip English Wikipedia <ref>{{Cite pmid|28361709}}</ref> Staged heterogeneity learning to identify conformational B-cell epitopes from antigen sequences #MMPMID28361709 Ren J; Song J; Ellis J; Li J BMC Genomics 2017[]; 18 (Suppl 2): ä PMID28361709show ga Background: 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. Results: 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. Conclusions: The proposed method uses only antigen sequence information, and thus has much broader applications. äStaged heterogeneity learning to identify conformational B-cell epitop(epmc).pdf DeepDyve Pubget Overpricing