Akshay Balsubramani

CodonBERT is a large language model for mRNA design and optimization that outperforms previous methods on a flu vaccine dataset.

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.

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

This paper introduces Yuzu, a method that accelerates in-silico saturation mutagenesis by using compressed sensing, reducing computation time for convolution-based models.

This study improves cross-species prediction of transcription factor binding by addressing domain shifts caused by species-specific repeats using domain adaptive neural networks.

This paper develops a statistical method to identify gene co-essentiality, creating a genome-wide functional module almanac that predicts functions for 102 uncharacterized genes and validates key findings.