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Research Paper|Volume 15, Issue 10|pp. 4465–4480
Identification and validation of diagnostic signature genes in non-obstructive azoospermia by machine learning
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Figure 3
Figure 3.Boruta feature selection and lasso regression. (A) Heatmap of Boruta screened genes. (B) Lasso regression. (C) PCA plot of signature genes. (D) Expression of signature genes (C12orf54, TSSK6, OR2H1, FER1L5, C9orf153 and XKR3) in training datasets. (P < 0.05, Abbreviations: NOA: non-obstructive azoospermia; CON: control group).
Figure 3 — Identification and validation of diagnostic signature genes in non-obstructive azoospermia by machine learning | Aging