Hillary Koch

The paper introduces a statistical model to measure reproducibility and replicability across replicate experiments in multi-source studies, using a nested copula mixture model.

This paper introduces CLIMB, a statistical method for detecting high-dimensional associations in genomic data, showing improved precision and biological relevance in hematopoietic datasets.

The paper introduces a spatial modeling approach for detecting differentially interacting genomic regions in Hi-C data with false discovery control, demonstrating its ability to reveal biologically meaningful changes in chromatin architecture.

The paper introduces SURFDAWave, a method to identify regions of positive selection by analyzing functional data derived from genomic diversity measures, and applies it to human data to detect adaptive introgression and predict selection parameters.

The paper introduces CFGL, a method for inferring condition-specific gene co-expression networks, showing improved accuracy and revealing biological insights in rat and cancer datasets.