Sagar R Shah

This study compares divergent and unidirectional transcriptional regulatory elements, finding that unidirectional elements are younger and less constrained, with some transcription factors modulating their directionality.

This paper introduces DeepDETAILS, a method to reconstruct cell-type-specific genomic signals from bulk data, outperforming traditional methods and providing insights into disease mechanisms.

This study identifies a SWI/SNF-targeting therapy effective against a double-negative prostate cancer subtype, showing it reduces viability in both AR-dependent and WNT-dependent models.

The paper presents a high-resolution atlas of transcriptional regulatory elements in human tissues and disease states using PRO-cap and deep learning models to prioritize noncoding variants.

The paper trains a deep learning model to predict transcription initiation from DNA sequences, revealing shared cis-regulatory logic and epistatic interactions across promoters and enhancers.