George C. Tseng

This paper introduces likelihood-based methods to detect circadian rhythmicity and differential circadian patterns in transcriptomic data, showing improved error control and power in simulations and real data.

This study investigates how NPAS2 mutations affect cocaine self-administration in mice, showing sex- and time-of-day-dependent changes in reward and motivation.

The paper introduces SAT, a method that uses both paired and unpaired data to improve association tests between biomedical data types, showing better control of false discovery and increased power in real-world applications.

The paper introduces CPI, a tool for integrating multiple transcriptomic studies to identify consensual and differential pathway enrichment patterns, and applies it to psychiatric disorder data.

BayICE improves gene expression deconvolution by addressing limitations in cell type gene selection and unknown cell profile recovery, showing better performance in estimating cell proportions in cancer data.

The paper identifies microRNAs whose target sites are modified by global APA events in breast cancer, showing their role in tumor progression and patient-specific heterogeneity.