A Bayesian framework for genome-wide circadian rhythmicity biomarker detection.

ABSTRACT: Circadian rhythms are endogenous $\sim $24-h cycles that significantly influence physiological and behavioral processes. These rhythms are governed by a transcriptional-translational feedback loop of core circadian genes and are essential for maintaining overall health. The study of circadian rhythms has expanded into various omics datasets, necessitating accurate analytical methodology for circadian biomarker detection. Here, we introduce a novel Bayesian framework for the detection of circadian rhythms in genome-wide transcriptomic applications that is capable of incorporating prior biological knowledge and adjusting for multiple testing issue via a false discovery rate (FDR) approach. Our framework leverages a Bayesian hierarchical model and employs a reverse jump Markov chain Monte Car

SUBMITTER: Ding H 

PROVIDER: S-EPMC12560793 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature