{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T20:54:51Z","timestamp":1770843291830,"version":"3.50.1"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"S2","content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2013,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Background<\/jats:title>\n            <jats:p>Development of computational tools that can accurately predict presence and location of B-cell epitopes on pathogenic proteins has a valuable application to the field of vaccinology. Because of the highly variable yet enigmatic nature of B-cell epitopes, their prediction presents a great challenge to computational immunologists.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods<\/jats:title>\n            <jats:p>We propose a method, BEEPro (<jats:underline>B<\/jats:underline>-cell <jats:underline>e<\/jats:underline> pitope prediction by <jats:underline>e<\/jats:underline> volutionary information and <jats:underline>pro<\/jats:underline> pensity scales), which adapts a linear averaging scheme on 16 properties using a support vector machine model to predict both linear and conformational B-cell epitopes. These 16 properties include position specific scoring matrix (PSSM), an amino acid ratio scale, and a set of 14 physicochemical scales obtained via a feature selection process. Finally, a three-way data split procedure is used during the validation process to prevent over-estimation of prediction performance and avoid bias in our experiment results.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>In our experiment, first we use a non-redundant linear B-cell epitope dataset curated by Sollner <jats:italic>et al.<\/jats:italic> for feature selection and parameter optimization. Evaluated by a three-way data split procedure, BEEPro achieves significant improvement with the area under the receiver operating curve (AUC) = 0.9987, accuracy = 99.29%, mathew's correlation coefficient (MCC) = 0.9281, sensitivity = 0.9604, specificity = 0.9946, positive predictive value (PPV) = 0.9042 for the Sollner dataset. In addition, the same parameters are used to evaluate performance on other independent linear B-cell epitope test datasets, BEEPro attains an AUC which ranges from 0.9874 to 0.9950 and an accuracy which ranges from 93.73% to 97.31%. Moreover, five-fold cross-validation on one benchmark conformational B-cell epitope dataset yields an accuracy of 92.14% and AUC of 0.9066.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>Compared with other current models, our method achieves a significant improvement with respect to AUC, accuracy, MCC, sensitivity, specificity, and PPV. Thus, we have shown that an appropriate combination of evolutionary information and propensity scales with a support vector machine model can significantly enhance the prediction performance of both linear and conformational B-cell epitopes.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1471-2105-14-s2-s10","type":"journal-article","created":{"date-parts":[[2013,1,21]],"date-time":"2013-01-21T15:16:08Z","timestamp":1358781368000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Prediction of B-cell epitopes using evolutionary information and propensity scales"],"prefix":"10.1186","volume":"14","author":[{"given":"Scott Yi-Heng","family":"Lin","sequence":"first","affiliation":[]},{"given":"Cheng-Wei","family":"Cheng","sequence":"additional","affiliation":[]},{"given":"Emily Chia-Yu","family":"Su","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2013,1,21]]},"reference":[{"issue":"Suppl2","key":"5599_CR1","doi-asserted-by":"publisher","first-page":"S2","DOI":"10.1186\/1745-7580-6-S2-S2","volume":"6","author":"Y EL-Manzalawy","year":"2010","unstructured":"EL-Manzalawy Y, Honavar V: Recent advances in B-cell epitope prediction methods. 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