Rohit Singh

This paper introduces Haystack, a method to prioritize causal transcription factors from single-cell data, validated in Drosophila, showing high accuracy in identifying drivers of differentiation.

The paper introduces a method to impute TAD architectures across cell/tissue types using existing epigenetic data and scRNA-seq, revealing gene clustering patterns in TADs during differentiation and in tumors.

D-SCRIPT predicts protein-protein interactions from sequence using a structure-aware deep learning model, improving cross-species generalization and functional characterization of proteins.

The paper organizes a DREAM challenge to benchmark predictive models of single-cell signaling using mass cytometry data from breast cancer cell lines.

The paper introduces Schema, a metric learning algorithm that synthesizes heterogeneous single-cell modalities to infer cell types, analyze gene expression, and estimate evolutionary pressure.