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. 2021 Feb 5;11:3253. doi: 10.1038/s41598-021-82939-1

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© The Author(s) 2021

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Univariate comparison and machine learning based on HBeAg seroconversion. (A) Wilcoxon test is conducted to identify the significant genera in paired groups (P vs. H, N vs. H, N vs. P). This procedure is repeated 100 times on bootstrapped sample set. About 18–22 genera are selected. The number of significant genera for each comparison is reported with boxplot. (B) Thirteen genera are selected as microbial signature for HBeAg seroconversion to guarantee about 71% accuracy rate on test data. (C) The mean of accuracy rate on test data is about 0.75, indicating this method is robust. (D) The area under the ROC curve (AUC) is 74.9%, which indicates the prediction model has a significant discriminative ability.