Shreshth Gandhi

The paper introduces Rhaister, a perturbation-response predictor that uses summary statistics to predict drug responses, outperforming virtual-cell models in speed and accuracy.

The paper introduces Tahoe-x1, a large-scale perturbation-trained single-cell foundation model with 3 billion parameters, achieving state-of-the-art performance in cancer-related tasks.

This paper introduces REPRESS, a deep learning model that predicts cell-type-specific microRNA binding and mRNA degradation from RNA sequences, outperforming existing methods in multiple tasks.

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.

This study evaluates off-target effects of steric-blocking oligonucleotides using RNA-seq and finds that in silico methods have limited utility for predicting these effects.

This paper shows that the ATP7B variant c.1934T>G causes exon 6 skipping, leading to loss of function and confirming its pathogenicity in Wilson Disease.

The paper introduces cDeepbind, a deep learning model that incorporates RNA secondary structure to improve RBP binding predictions, achieving better performance on both in-vitro and in-vivo data.

COSSMO predicts competitive alternative splice site selection using deep learning, achieving 70% accuracy in identifying the most used splice site and 60% R2 in modeling PSI distribution.