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Fig. 5 | BMC Medical Genomics

Fig. 5

From: Proteomic analysis to identification of hypoxia related markers in spinal tuberculosis: a study based on weighted gene co-expression network analysis and machine learning

Fig. 5

The key hypoxia-related proteins and prediction models. (A) Differential expression of 11 genes in extrapulmonary TB and control group in the GSE144127 dataset. (B) SVM-REF algorithm for screening key genes. (C) LASSO coefficient spectrum of 11 differentially expressed genes selected by optimal. (D) Selection of the best parameter. (E) PSMB9, STAT1, and TAP1 were screened by two algorithms and MCODE. (F-H) Differential expression of PSMB9, STAT1, and TAP1 between TB group and control group in the GSE83456 dataset. (I, J) Diagnostic ROC curves of PSMB9, STAT1, and TAP1 in extrapulmonary TB and TB. (K, L) Accuracy of PSMB9, STAT1, and TAP1 prediction models based on 5 machine learning algorithms for extrapulmonary TB and TB.

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BMC Medical Genomics

ISSN: 1755-8794

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