Traditional gene set enrichment analyses are typically limited to a few ontologies and do not account for the interdependence of gene sets or terms, resulting in overcorrected p-values. To address these challenges, we introduce mulea, an R package offering comprehensive overrepresentation and functional enrichment analysis. mulea employs a progressive empirical false discovery rate (eFDR) method, specifically designed for interconnected biological data, to accurately identify significant terms within diverse ontologies.
View Article and Find Full Text PDFBackground: In the past decade, the prevalence of end-stage inflammatory elbow arthritis has declined with consequential changes in indications and utilization of total elbow arthroplasty (TEA). Current literature lacks future projections for the utilization of TEA. The aim of this study is to review the trends in the utilization of TEA in the last 2 decades and determine the projections of utilization for TEA (primary and revision) through 2060.
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