Laksshman Sundaram

The paper maps gene expression responses to loss of 1,947 disease-associated genes across the mouse brain using Perturb-seq, revealing cell-type-specific essentiality and transcriptional programs.

The paper uses deep learning to analyze chromatin accessibility and gene expression during reprogramming, revealing how TF concentrations and motif syntax influence cis-regulatory networks.

The paper uses primate genetic data to predict human variant pathogenicity with a deep learning classifier trained on 4.3 million common missense variants.

This study uses single-cell chromatin accessibility analysis to identify developmental trajectories and non-coding mutations associated with congenital heart disease.

The paper maps gene-regulatory dynamics in the developing human cerebral cortex using single-cell data, identifying key transcription factors and disrupted regulatory elements in ASD.