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. 2019 Jul 29;9:10972. doi: 10.1038/s41598-019-47287-1

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© The Author(s) 2019

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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

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Machine learning effect size analyses of data. (Left) Rankings of parameters determined from a Relief-F algorithm (s = soluble; t = total; i = insoluble). GFAP had the highest weighting followed by Iba1. (Right) Linear discriminant analysis using the top two parameters (GFAP, Iba1) and Aβ40s and Aβ40/42 by fingolimod dose (Wilk’s lambda = 0.169; p < 0.0001 for function 1, 0.769, p < 0.006 for function 2). The largest average distance from control was for the 0.03 mg/kg/day dose of fingolimod.