Additional file 2 of Developing neural network diagnostic models and potential drugs based on novel identified immune-related biomarkers for celiac disease

Abstract

Additional file 2: Fig. S1. GO and KEGG analysis of 58 differentially expressed immune-related genes. A GO enrichment results in differentially expressed immune-related genes. B KEGG enrichment results in differentially expressed immune-related genes. Fig. S2. Heatmap shows the overall landscape of CD patients' ssGSEA score of 28 immune gene sets. Fig. S3. Consensus matrix heatmap when K = 3–9. It is related to Fig. 3D. Fig. S4. The box plot shows the ssGSEA score of immune cells of the C1 and C2 groups. (ns, no significance, *P < 0.05, **P < 0.01, ***P < 0.001). Fig. S5. Validation of the IG score in the GSE164883 set. A The violin plot shows the IG score between the control and CD groups. B The ROC curve of the IG score in the GSE164883 validation set. Fig. S6. ROC analysis validated the diagnostic performance of HIGs. ROC curves of the indicated HIGs in the GSE11501 training set (A) and GSE164883 validation set (B). Fig. S7. Construction of artificial neural network (ANN) based on HIGs. A The construction of an artificial neural network (ANN) based on MR1, TNFSF13B, and CCL25. B The AUC of the training cohort with a value of 0.824. C The AUC of the test cohort with a value of 0.733. Fig. S8. 3D (left) and 2D (right) structure of complexes of HIGs and drugs. It is related to Fig. 7