Marc D. Ruben

This study uses machine learning to predict sleep-related genes from genomic data and validates the NF-κB pathway's role in sleep duration in mice.

This paper introduces LTM, an in silico method to identify genetic influences on circadian clock function using gene expression data, and finds the cell cycle and extracellular matrix pathways are strongly associated with clock strength in human tissues.

The paper introduces nCV, a method to assess circadian clock robustness in population-scale data without time labels, finding dampened oscillations in tumors.

The paper develops a 12-gene circadian biomarker from epidermal samples that accurately estimates circadian phase across body sites, ages, sexes, and platforms.

The paper identifies robust circadian rhythms in human skin at the population level and develops single-sample biomarkers for circadian phase.

The paper identifies rhythmic gene expression across human tissues using computational methods and highlights implications for circadian medicine in cardiovascular disease.