3 citations · 5 across the 3 of their papers we have counts for
3 papers
A scalable gene network model of regulatory dynamics in single cells
Paul Bertin, Joseph D. Viviano, Alejandro Tejada-Lapuerta +4
Single-cell data provide high-dimensional measurements of the transcriptional states of cells, but extracting insights into the regulatory functions of genes, particularly identify…
Interpretable Neural ODEs for Gene Regulatory Network Discovery under Perturbations
Zaikang Lin, Sei Chang, Aaron Zweig +4
Modern high-throughput biological datasets containing thousands of perturbations enable large-scale discovery of causal graphs that represent regulatory interactions between genes.…
Causal machine learning for single-cell genomics
Alejandro Tejada-Lapuerta, Paul Bertin, Stefan Bauer +3
Advances in single-cell omics allow for unprecedented insights into the transcription profiles of individual cells. When combined with large-scale perturbation screens, through whi…