WE ARE RECRUITING GRAD STUDENTS AND UNDERGRAD RESEARCHERS
to develop neural networks for understanding protein-DNA interactions (see details)
RESEARCH THEMES
Applying machine learning
to regulatory genomics
Can we mine large genomic datasets to find subtle patterns that explain biological processes? We are computational biologists who develop neural networks and other machine learning applications for understanding gene regulation. Read more …
Characterizing determinants
of protein-DNA interactions
How do transcription factors know where to bind to the genome in a given cell type? We study how TF regulatory targets are influenced by DNA-binding preferences, interactions with other proteins, and cell-specific chromatin environments. Read more …
Understanding cell fate decisions
How do regulatory signals modify cell fate during development? Can we artificially program cellular identity? In collaborative projects, we explore how transcription factors convert cells into neurons and other cell types. Read more …
FUNDING SOURCES
NSF DBI CAREER 2045500
CAREER: Predicting transcription factor binding dynamics across cell types and species
NIH NIGMS MIRA R35-GM144135
Understanding the predeterminants of transcription factor regulatory activity