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. 2022 Dec 13;20(5):1002–1012. doi: 10.1016/j.gpb.2022.11.009

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© 2022 The Authors

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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Performance comparison between NetBCE and other toolsand the display interface of NetBCE software

A. Comparison of NetBCE with other predictors, including BepiPred, LBtope, iBCE-EL, and EpiDope on the independent dataset regarding the ROC curves. B. Comparison of NetBCE with other predictors regarding the PR curves. C. Feature representation of the epitopes and non-epitopes in the independent dataset using the UMAP method in the input layer of NetBCE. D. Feature representation of the epitopes and non-epitopes (independent dataset) in the BLSTM layer. E. Feature representation of the epitopes and non-epitopes (independent dataset) in the fully connected layer. F. Feature representation of the epitopes and non-epitopes (independent dataset) in the final classification layer. G. The display interface of NetBCE software. NetBCE provides and visualizes the prediction results in an interactive HTML file using the Python, PHP, JavaScript, and Bootstrap package in an easily readable and interpretable manner.