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. 2023 Jul 12;14:1168780. doi: 10.3389/fimmu.2023.1168780

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Copyright © 2023 Zhou, Huang, Li, Song, Yang, Wang and Meng

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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Machine Learning Screening Hub OA-ARDEGs. (A) LASSO coefficient analysis. Vertical dashed lines are plotted at the best lambda. (B) Ten cross-validations of the choice of adjustment parameters in the LASSO model. Each curve corresponds to one gene. (C, D) Maximum accuracy and minimum error plots of the SVM-RFE algorithm for screening optimal OA-ARDEGs. (E) Ranking of the relative importance of OA-ARDEGs. (F) Relationship between the number of random forest trees and the error rate. (G) LASSO, Random Forest, and SVM-RFE algorithms for screening Venn diagrams of the intersection of Aging signature genes.