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. 2023 Mar 2;14:1114601. doi: 10.3389/fgene.2023.1114601

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Copyright © 2023 Fu, Chen, Cheng, Yang, Zhu, Wu and Cai.

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 algorithms identify disease candidate genes. (A) LASSO model for screening candidate genes for disease. (B) SVM-RFE algorithm for screening disease candidate genes. (C) Random forest model to screen candidate genes for disease. (D) The Venn diagram shows the common disease candidate genes of LASSO, RandomForest and SVM-RFE algorithms. (E) Boxplot representing expression of disease candidate genes in training set. (F) Boxplot representing expression of disease candidate genes in the validation set.