Ruchir Rastogi

This paper introduces Swap-seq, a method to systematically delete and quantify the effects of enhancers, silencers, and topological elements on gene expression in human cells.

A fully orthogonal IL-2 receptor platform isolates 36 cytokine receptors in T cells; receptor-specific STAT combinations specify distinct fates, and STAT3-dominant programs give curative antitumor responses.

The paper identifies distinct adipocyte subsets in human white adipose tissue with different metabolic profiles and links TSHZ3 to their regulation and metabolic disease associations.

This study fine-tunes sequence-to-expression models on personal genome and transcriptome data, showing improved performance on seen genes but not on unseen genes.

The paper evaluates missense variant effect predictors using CAGI6 data, finding strong performance across settings and highlighting biases in some predictors.

This study examines uncertainty in genomic sequence-to-activity model predictions by analyzing consistency across different sequence types and identifies areas where models perform poorly.

This study evaluates genomic deep learning models' ability to explain individual expression variation and finds limited performance.

This paper introduces a cross-protein transfer learning framework that improves disease variant prediction using deep mutational scanning data from few proteins, achieving better specificity than existing models.