Comparison of High-Throughput Technologies in the Classification of Adult-Onset Still's Disease Patients.

A meta-analysis study was conducted to compare high-throughput technologies in the classification of Adult-Onset Still's Disease patients, using differentially expressed genes from independent profiling experiments. We exploited two publicly available datasets from the Gene Expression Omnibus and performed a separate differential expression analysis on each dataset to extract statistically important genes. We then mapped the genes of the two datasets and subsequently we employed well-established machine learning algorithms to evaluate the denoted genes as candidate biomarkers. Using next-generation sequencing data, we managed to achieve the maximum (100%) classification accuracy, sensitivity and specificity with the Gradient Boosting and the Random Forest classifiers, compared to the 83% of the DNA microarray data. Clinical Relevance- When biomarkers derived from one study are applied to the data of another, in many cases the results may diverge significantly. Here we establish that in cross-profiling meta-analysis approaches based on differential expression analysis, next-generation sequencing data provide more accurate results than microarray experiments in the classification of Adult-Onset Still's Disease patients.