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. 2020 Oct 15;10:17505. doi: 10.1038/s41598-020-73588-x

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© The Author(s) 2020

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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Age Prediction Models. (A) The graph represents the performance of a linear regression model for age prediction. The model was constructed from 5 different glycopeptides (IgG1 g:3510, IgG1 g:5410, IgM p:209 g:5411, IgM J chain g:5412, Hp p:241 g:7602). Diagnostic plots (residuals vs fitted, testing for linearity; normal Q-Q, to assess the distribution of the residuals; scale-location, to assess the homoscedastic of the data; and residuals vs leverage, to check for overly influential cases) for the model are presented to its right. (B) Linear regression model comprised of six glycopeptides (IgG1 g:3510, IgG1 g:5410, IgG2 g:3410, IgM p:209 g:5411, IgM J chain g:5412, Hp p: 241 g:7602) and 1 serum protein, IgG3. Model diagnostics are represented to the right (model performance parameters for age prediction models can be found in Table S7).