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Plasma proteomic profiling suggests an association between antigen driven clonal B cell expansion and ME/CFS

Fig 2

Diagnostic performance (AUROC) of ME/CFS and ME/CFS subgroup plasma proteomes.

Three machine learning algorithms were used to examine the utility of the proteomics assay as a biomarker tool for ME/CFS: Lasso (least absolute shrinkage and selection operator), Random Forests, and XGboost. We fitted all protein analytes, excluding the ones with more than 50% undetectable/filtered values, as predictors in the three classifiers and measured the importance for each predictor in the classifiers. The protein analytes that were ranked in the top 20 in all three importance measurements were fitted in the classifiers again (Trimmed set), except that here we used the logistic regression model instead of Lasso. The predictive performance was evaluated in random resampling cross-validation (CV) with 1,000 iterations from which we calculated the Area under the Receiver Operating Characteristic curve (AUROC) values and generated Receiver Operating Characteristic (ROC) curves for (A) all ME/CFS cases, (B) ME/CFS cases with sr-IBS and (C) ME/CFS cases without sr-IBS. ME/CFS: myalgic encephalomyelitis/chronic fatigue syndrome, sr-IBS: self-reported irritable bowel syndrome.

Fig 2