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Fig. 4 | Journal of Translational Medicine

Fig. 4

From: Critical role of Oas1g and STAT1 pathways in neuroinflammation: insights for Alzheimer’s disease therapeutics

Fig. 4

Hub genes were screened using ML and validated in the validation dataset. (A) Employing the SVM-RFE algorithm for biomarker screening. The x-axis represents the number of feature selections, while the y-axis displays the prediction accuracy. (B) Screening Biomarkers Using the Lasso Algorithm. The graphic on the left displays a coefficient curve for each individual gene. In the right picture, the solid vertical lines indicate the partial likelihood of deviance. The number of genes (n = 3) that corresponds to the lowest point of the cure is considered the most appropriate. The solid vertical lines in the right figure depict the partial likelihood of deviation, whereas the lowest point on the curve determines the ideal number of genes. (C) The Venn diagram demonstrates that candidate diagnostic genes are identified using SVM-RFE and Lasso algorithms. (D) AUC was 0.812 with a 95% CI of 0.6324–0.9926. (E) Oas1g expression was higher in AD mouse microglia than in control mouse microglia in the validation dataset (p = 0.0083)

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