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. 2022 Oct 6;12:16693. doi: 10.1038/s41598-022-21050-5

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© The Author(s) 2022

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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Machine learning approach to screen for NSCLC-related biomarkers. The point corresponding to the smallest vertical coordinate is the characteristic genes. (a) Results of screening biomarkers based on LASSO algorithm. (b) Screening results for biomarkers based on the SVM-RFE algorithm. (c) The Venn diagram shows the results of the intersection of the LASSO algorithm and the SVM-RFE algorithm, with the intersection resulting in eight biomarkers.