ArticlePeerJ2026
False discovery rate control for grouped hypotheses: application to miRNAome data.
Article in PeerJ, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
4 authors.
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Abstract
Bioinformatics studies often involve numerous simultaneous statistical tests, increasing the risk of false discoveries. To control the false discovery rate (FDR), these studies typically apply a statistical method called the Benjamini-Hochberg (BH) method. However, BH can be overly conservative, particularly in small-sample studies, and it does not take advantage of relevant structural information among the hypotheses, such as groupings. Group structures can arise, for example, when genomic features located in close proximity are co-regulated. Recent statistical developments have yielded group-adaptive BH methods that can leverage pre-existing group information to improve statistical power while maintaining FDR control. However, these methods remain underutilized in bioinformatics practice. In this study, we illustrate the practical application of group-adaptive BH methods using a previously published, moderately scaled microRNA (miRNA) dataset. Even under simple groupings based on chromosomal location, these methods identified more miRNAs with significantly deregulated expression (FDR-adjusted
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