Emanuelle I. Grody

ExPert infers causal perturbations from cell states by learning prototypes across 12 contexts, achieving high recall with low compute and validating findings in disease models.

CIPHER uses gene co-fluctuations in unperturbed cells to predict transcriptome-wide responses to perturbations, outperforming existing methods and revealing key regulatory structures.

The paper introduces dynamo, a framework for estimating RNA velocities and predicting cell fates using single-cell RNA-seq data, applied to various biological processes.

Raman2RNA predicts single-cell RNA expression profiles from live-cell Raman microscopy images using machine learning, achieving high accuracy in tracking cell state transitions during reprogramming.

ClusterMap is a framework for analyzing spatial gene expression data, clustering RNAs into subcellular structures and tissue regions across multiple dimensions.