Easwaran Ramamurthy

The paper identifies context-dependent regulatory variants in Alzheimer's disease by analyzing their effects across different cellular contexts using MPRAs and machine learning.

The paper introduces WhAMPRA, an in vivo MPRA platform in mice, to assess tissue-specific regulatory functions of genetic variants and enhancers.

The paper uses CNN regression models to predict the impact of AD-associated variants on open chromatin in different cell types, finding greater effects in peripheral cells than in microglia or neurons.

The paper introduces BigRNA, an RNA foundation model that predicts RNA biology from DNA sequence and identifies therapeutic effects of SBOs on gene expression and splicing.

The paper profiles H3K27ac in brain cell types from AD patients and finds acetylation differences in oligodendrocytes linked to AD risk genes and pathways.

The paper introduces SNAIL, a virus-based method using machine learning to design cell type-specific enhancer probes for targeted nuclear isolation and characterization.

The paper identifies addiction-associated genetic variants in brain cell type- and region-specific cis-regulatory elements using GWAS and machine learning.