Publications

By focusing on causal relationships in genome regulation, a new AI method could help scientists identify new immunotherapy techniques or regenerative therapies.

Adam Zewe

Oct 21, 2023

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Researchers identify new regulators of cellular aging

During aging, changes in gene expression are tied to alterations in the packing of DNA, a new study shows.

Giorgia Guglielmi

Jan 10, 2024

Publications.

DSMBind: SE(3) denoising score matching for unsupervised binding energy prediction and nanobody design

Single-cell image-based genetic screens systematically identify regulators of Ebola virus subcellular infection dynamics

Unsupervised representation learning of chromatin images identifies changes in cell state and tissue organization during DCIS progression

A mechanism for producing aligned latent spaces with autoencoders

Causal discovery with fewer independence tests

Season combinatorial intervention predictions with Salt & Peper

Membership testing in Markov equivalence classes via independence queries

Causal discovery under off-target interventions

Removing biases from molecular representations via information maximization

Causal imputation for counterfactual SCMs: Bridging graphs and latent factor models

Building a two-way street between cell biology and machine learning

Direct estimation of differences in causal graphs

PatchNet: Interpretable neural networks for image classification

Minimal I-MAP MCMC for scalable structure discovery in causal DAG models

Characterizing and learning equivalence classes of causal DAGs under interventions

Nuclear mechanopathology and cancer diagnosis

Generalized permutohedra from probabilistic graphical models

Exact formulas for the normalizing constants of Wishart distributions for graphical models

Gaussian graphical models: An algebraic and geometric perspective

Adhesome receptor clustering is accompanied by the co-localization of the associated genes in the cell nucleus

Systematically characterizing the roles of E3-ligase family members in inflammatory responses with massively parallel Perturb-seq

Unsupervised representation learning of chromatin images identifies changes in cell state and tissue organization during DCIS progression

Image2Reg: Linking chromatin images to gene regulation using genetic perturbation screens

Maximum likelihood estimation for Brownian motion tree models based on one sample

Efficient permutation discovery in causal DAGs

Linear convergence and implicit regularization of generalized mirror descent with time-dependent mirrors

Transcriptional changes are tightly coupled to chromatin reorganization during cellular aging

Meek separators and their applications in targeted causal discovery

Identifiability guarantees for causal disentanglement from soft interventions

Unpaired multi-domain causal representation learning

Unsupervised protein-ligand binding energy prediction via neural Euler’s rotation equation

Active learning for optimal intervention design in causal models

The DeCAMFounder: Non-linear causal discovery in the presence of hidden variables

Transfer Learning with Kernel Methods

Linear causal disentanglement via interventions

Cross-modal autoencoder framework learns holistic representations of cardiovascular state

Wide and deep neural networks achieve optimality for classification

Causal structure learning: a combinatorial perspective

Graph-based autoencoder integrates spatial transcriptomics with chromatin images and identifies joint biomarkers for Alzheimer’s disease

Lateral confined growth of cells activates Lef1 dependent pathways to regulate cell-state transitions

Machine learning model finds genetic factors for heart disease

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A more effective experimental design for engineering a cell into a new state

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Researchers identify new regulators of cellular aging

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Research in the Uhler Lab is supported by