IgG Antibody Responses to Epstein-Barr Virus in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Their Effective Potential for Disease Diagnosis and Pathological Antigenic Mimicry

Figure 1. Analysis of all ME/CFS patients versus HCs. (A) Density plot of each antibody’s average importance distribution obtained by the RF with the top 10 most important antibodies highlighted. (B) Accuracy of the SL classifier in the train (purple) and test (orange) subsets as a function of the number of antibodies included. The black and blue horizontal dashed lines indicate 85% (target accuracy). The best classifier is highlighted with a black dot. (C) Accuracy of the different classifiers assembled by the SL in the train subset. (D) Sensitivity and specificity of the SL classifiers in the test subset as a function of the number of antibodies included.

Figure 2. Analysis of ME/CFS patients with non-infectious or unknown disease triggers against HCs. (A) Density plot of each antibody’s average importance distribution obtained by the RF with the top 10 most important antibodies highlighted. (B) Accuracy of the SL classifier in the train (purple) and test (orange) subsets as a function of the number of antibodies included. The black and blue horizontal dashed lines referred to the 85% target accuracy. The best classifier is highlighted with a black dot. (C) Sensitivity and specificity of the SL classifiers in the test subset as a function of number of antibodies included.

Figure 3. Analysis of ME/CFS patients with an infectious disease trigger against HCs. (A) Density plot of each antibody’s average importance distribution obtained by the RF with the top 10 most important antibodies highlighted. (B) Accuracy of the SL classifier in the train and test subsets as a function of the number of antibodies included. The black and blue horizontal dashed lines indicate the target accuracy of 85% (i.e., 0.85). The best classifier is highlighted with a black dot. (C) Boxplots of the log10-levels of the selected 26 antibodies in HCs and ME/CFS patients. (D) Confusion matrix concerning the optimal classifier performance on the test subset. (E) ROC curve of the optimal classifier for both train and test subsets.

Figure 4. Bioinformatic analysis of the EBV peptides associated with the 26 antibodies for predicting ME/CFS patients with an infectious disease trigger. (A) Alignments between EBNA6_0070 and the human proteins HOXA-9A and ADRA1B_371 with the corresponding amino-acid coordinates within brackets. (B) Alignments between EBNA6_0488* and the human proteins CTCF and AEBP1 with the corresponding protein coordinates within brackets. (C). Scatterplot between the average importance of each EBV peptide and the maximum E-score alignment score with human proteins using the RefSeq reference protein database, where R is the Spearman correlation coefficient with the respective 95% confidence interval in brackets.