Eeshit Dhaval Vaishnav

The paper evaluates and optimizes sequence-based gene regulatory deep learning models using a DREAM Challenge dataset, showing improvements through architectural and training strategy refinements.

SCHAF generates spatially resolved single-cell transcriptomes from H&E histology images using vision transformers and adversarial learning, showing good agreement with ground-truth data.

This study evaluates six algorithms for cell type matching in spatial transcriptomics data and presents consensus strategies for improved analysis.

The paper creates a comprehensive atlas of mouse primary motor cortex cell types using integrated transcriptomic and epigenomic data from over 600,000 samples.

The paper constructs a deep transformer model to predict gene expression from cis-regulatory DNA sequences in yeast, revealing evolutionary constraints and evolvability patterns.

Across 1,176,683 airway and lung cells from 22 datasets, ACE2 rises with age, in males and in smokers' multiciliated cells, and is lowest in young children.

The paper uses random yeast promoters to model cis-regulatory logic, achieving high prediction accuracy for gene expression.