Search Results for author:
Found 7 papers, 4 papers with code
Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling
Instead, recent methods propose to leverage non-stationary data, as well as the sparse mechanism shift assumption in order to learn disentangled representations with a causal semantic.
Large-Scale Differentiable Causal Discovery of Factor Graphs
Combining this novel structural assumption with recent advances that bridge the gap between causal discovery and continuous optimization, we achieve causal discovery on thousands of variables.
The evolution, evolvability and engineering of gene regulatory DNA
Mutations in non-coding regulatory DNA sequences can alter gene expression, organismal phenotype and fitness.
Cell types and ontologies of the Human Cell Atlas
Massive single-cell profiling efforts have accelerated our discovery of the cellular composition of the human body, while at the same time raising the need to formalise this new knowledge.
Inference of cell dynamics on perturbation data using adjoint sensitivity
While there is no analogous ground truth for real life biological systems, this work demonstrates the ability to construct and parameterize a considerable diversity of network models with high predictive ability.
Mapping the Human Body at Cellular Resolution -- The NIH Common Fund Human BioMolecular Atlas Program
Transformative technologies are enabling the construction of three dimensional (3D) maps of tissues with unprecedented spatial and molecular resolution.
Reconstructing probabilistic trees of cellular differentiation from single-cell RNA-seq data
Specifically, we extend the framework of the classical Dirichlet diffusion tree to simultaneously infer branch topology and latent cell states along continuous trajectories over the full tree.
