Positive-unlabeled learning for the prediction of conformational B-cell epitopes

BACKGROUND: The incomplete ground truth of training data of B-cell epitopes is a demanding issue in computational epitope prediction. The challenge is that only a small fraction of the surface residues of an antigen are confirmed as antigenic residues (positive training data); the remaining residues...

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Detalles Bibliográficos
Autores principales: Ren, Jing, Liu, Qian, Ellis, John, Li, Jinyan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4682424/
https://www.ncbi.nlm.nih.gov/pubmed/26681157
http://dx.doi.org/10.1186/1471-2105-16-S18-S12

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4682424/
https://www.ncbi.nlm.nih.gov/pubmed/26681157
http://dx.doi.org/10.1186/1471-2105-16-S18-S12

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