Elias Ventre

GSET retrieves dynamic gene signals to improve mechanistic GRN inference by identifying bridge genes involved in cell-state transitions.

CardamomOT improves gene regulatory network inference and trajectory reconstruction by integrating mechanistic models with optimal transport, using exact time labels and reducing hyperparameters.

This paper introduces a method to infer cell trajectories from aging time courses, improving accuracy in noisy data by leveraging temporal information.

This paper introduces a method for cell trajectory inference using optimal transport with a mechanistic stochastic gene expression model, showing the Bursty model better captures gene dynamics than the Diffusive model.

This paper combines two methods to use a single gene regulatory network model for both inference and simulation of transcriptional profiles from time-course single-cell data.

The paper reduces a stochastic gene expression model to a discrete coarse-grained model, using Lagrangian dynamics to identify basins of attraction as cell types and calculate metastability.

The paper introduces CARDAMOM, a method for inferring Gene Regulatory Networks from timestamped scRNA-seq data by leveraging metastability and transcriptional bursting.