Presented By: DCMB Seminar Series
Weekly DCMB Seminar with Refreshments
Michael A. Beer (Professor at Johns Hopkins), "Sequence-based Machine Learning for Modeling Cell State Transitions"
Abstract:
Most disease associated genomic variants have relatively modest effects on target gene expression in reporter or CRISPR perturbation assays. In addition, enhancer disruption in vivo often has surprisingly weak phenotypic consequences. I will present machine learning (ML) methods (gkm-SVM and DNN) which we use to learn the complex transcription factor combinations that control enhancer activity and cell fate. I will then use these methods to develop a quantitative model for enhancer activity which shows that while promoter knockdown has robust effects on target gene expression, individual enhancer knockdown is often weaker and affects temporal transition dynamics, but not the final steady state. This model provides an explanation of the paradox of how enhancer variation can be strongly associated with disease risk while having individually weak effects, by showing in detail how gene regulatory networks control developmentally important and disease relevant cell state transitions and cancer.
https://umich-health.zoom.us/j/93929606089?pwd=SHh6R1FOQm8xMThRemdxTEFMWWpVdz09
Most disease associated genomic variants have relatively modest effects on target gene expression in reporter or CRISPR perturbation assays. In addition, enhancer disruption in vivo often has surprisingly weak phenotypic consequences. I will present machine learning (ML) methods (gkm-SVM and DNN) which we use to learn the complex transcription factor combinations that control enhancer activity and cell fate. I will then use these methods to develop a quantitative model for enhancer activity which shows that while promoter knockdown has robust effects on target gene expression, individual enhancer knockdown is often weaker and affects temporal transition dynamics, but not the final steady state. This model provides an explanation of the paradox of how enhancer variation can be strongly associated with disease risk while having individually weak effects, by showing in detail how gene regulatory networks control developmentally important and disease relevant cell state transitions and cancer.
https://umich-health.zoom.us/j/93929606089?pwd=SHh6R1FOQm8xMThRemdxTEFMWWpVdz09
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