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. 2023 Sep 22;10:1275897. doi: 10.3389/fmolb.2023.1275897

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Copyright © 2023 Zhang, Jiang, Jiang, Chen, Huang, Zhang, Wang, Chi, Yang and Tian.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Construction and validation of the diagnostic model. (A) Construction of the artificial neural network (ANN) model, which contains the input layer, hidden layer and output layer. (B) Training set receiver operating characteristic curve (ROC), the size of the area under the curve (AUC) demonstrates the feasibility of the model. (C) Validation set ROC, the size of AUC can indicate the applicability of the model. (D) Diagnostic line graph of HBV-LC feature genes, each gene corresponds to a score, and the scores of all genes are summed to obtain the total score. (E) Calibration curve to assess the predictive performance of the diagnostic line graph. (F) Decision curve analysis (DCA), which compares the clinical benefit of the diagnostic line graph model with other diagnostic indicators.