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. 2022 Dec 5;13:1014509. doi: 10.3389/fimmu.2022.1014509

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Copyright © 2022 Wang, Mo, Zhang and Bai

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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Screening of disease signature genes. (A) Selection of the best Log (ʎ) value for LASSO regression. The x-axis represents the Log (ʎ) value, and the y-axis represents the error rate of cross-validation errors. (B) The influence of the number of decision trees on the error rate. The x-axis repre- sents the number of decision trees, and they-axis indicates the error rate. (C) Results of the Ginico efficient method in random forest classifier. The x-axis indicates the genetic variable, and the y-axis represents the importance index. (D) Variation curve of gene cross-validation error in SVM-RFE algorithm. (E) Venn diagram showing 3 disease signature genes shared by LASSO, SVM-RFE and RF arithmetic methods.