Comparison of Proteomic Approaches in Autoinflammatory Disease Classification

Abstract

A cross-analysis study was conducted to compare proteomic platforms in classifying patients with Systemic Autoinflammatory diseases, using proteins extracted from different profiling experiments. The datasets used were obtained from SomaScan assays and Mass Spectrometry (MS). A separate analysis was performed to each dataset based on the false discovery rate (FDR) in order to extract statistically important proteins. Conventional machine learning algorithms were subsequently employed to evaluate the denoted proteins as candidate biomarkers and compare the predictive capabilities of the two proteomic platforms. Using the SomaScan assay, we managed to achieve higher classification metrics compared to the MS dataset. An improvement was also attained on the classification results when the features used were extracted from the MS data and applied on the SomaScan dataset, compared to the opposite combination. Finally, the proteins derived from the FDR analysis in both datasets proved to be highly correlated regarding their importance score.