Paper Info
Title
Comparison of High-Throughput Technologies in the Classification of Adult-Onset Still's Disease Patients
Abstract
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.
Year
DOI
Venue
2022
10.1109/EMBC48229.2022.9871152
2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Keywords
DocType
Volume
Biomarkers,Gene Expression Profiling,Humans,Machine Learning,Oligonucleotide Array Sequence Analysis,Still's Disease, Adult-Onset
Conference
2022
ISSN
ISBN
Citations 
2375-7477
978-1-7281-2783-5
0
PageRank 
References 
Authors
0.34
4
4
Name
Order
Citations
PageRank
Orestis D Papagiannopoulos100.34
Konstantina Kourou200.34
Costas Papaloukas300.68
Dimitrios I. Fotiadis404.39