Award
National Human Genome Research Institute 5R01HG012367-04
Sequence-based Machine Learning for Inference of Dynamic Cell State Gene Network Models
Recipient
Johns Hopkins University, Baltimore, MD
Award Amount
$449,094.00
Ceiling
$449,094.00
Awarded
April 14, 2025
Identifier
5R01HG012367-04
This award funds research to develop advanced machine learning models to understand gene regulatory networks and predict the effects of genetic variations on enhancer activity and gene expression during cell state transitions, contributing to insights into disease mechanisms.
Description
Develop computational methods to infer quantitative models of combinatorial interactions of transcription factors and enhancers by training on temporally-resolved measurements of gene activity, enhancer activity, and core cell fate-regulating transcription factor activity across cell state transitions in early human development. The project aims to improve sequence-based machine learning models to build gene network models to predict the impact of enhancer perturbation by CRISPR or genetic variation, validated by high time resolution chromatin accessibility and other data in stem cell differentiation and large consortium datasets.