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

LINK TO ARTICLE

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

Simple, fast, and flexible framework for matrix completion with infinite width neural networks

Machine learning approaches to single-cell data integration and translation

Joint inference of multiple graphs from matrix polynomials

Causal structure discovery between clusters of nodes induced by latent factors

Causal imputation via synthetic interventions

Identifying 3D genome organization in diploid organisms via Euclidean distance geometry

Multiscale simulations of complex systems by learning their effective dynamics

Improved conditional flow models for molecule to image synthesis

DCI: Learning causal differences between gene regulatory networks

Total positivity in exponential families with application to binary variables

Causal network models of SARS-CoV-2 expression and aging to identify candidates for drug repurposing

Matching a desired causal state via shift interventions

Near-optimal multi-perturbation experimental design for causal structure learning

Causality in digital medicine

Maximum likelihood estimation for totally positive log-concave densities

Do deeper convolutional networks perform better?

On alignment in deep linear neural networks

Local quadratic convergence of stochastic gradient descent with adaptive step size

Consistency guarantees for greedy permutation-based causal inference algorithms

Multidomain translation between single-cell imaging and sequencing data using autoencoders

Mechanogenomic coupling of lung tissue stiffness, EMT and coronavirus pathogenicity

Ordering-based causal structure learning in the presence of latent variables

Permutation-based causal structure learning with unknown intervention targets

Causal structure discovery from distributions arising from mixtures of DAGs

Optimal transport using GANs for lineage tracing

Overparameterized neural networks can implement associative memory

Covariance matrix estimation under total positivity for portfolio selection

Learning high-dimensional Gaussian graphical models under total positivity without tuning parameters

Anchored causal inference in the presence of measurement error

Brownian motion tree models are toric

Predicting cell lineages using autoencoders and optimal transport

Mechano-genomic regulation of coronaviruses and its interplay with ageing

High-dimensional joint estimation of multiple directed Gaussian graphical models

Algebraic statistics in practice: Applications to networks

Maximum likelihood estimation in Gaussian models under total positivity

Multi-domain translation by learning uncoupled autoencoders

Memorization in overparameterized autoencoders

ABCD-Strategy: Budgeted experimental design for targeted causal structure discovery

Size of interventional Markov equivalence classes in random DAG models