Projects
Cancer genomics
We reconstruct the evolutionary history of tumors to understand how different populations of cells evolve over time.
Post-transcriptional regulation
We develop computational methods to understand the regulatory mechanisms of RNA-binding proteins, 3'-end mRNA processing, and alternative splicing.
Genotype-to-phenotype
We explore mutant phenotypes of single-deletion strains of S. cerevisiae by applying novel machine learning approaches.
Precision medicine
We use unsupervised learning to integrate biological and clinical data and find innovative predictors of disease trajectories.
