5 papers
Theoretical Guarantees for Causal Discovery on Large Random Graphs
Mathieu Chevalley, Arash Mehrjou, Patrick Schwab
We investigate theoretical guarantees for the false-negative rate (FNR) -- the fraction of true causal edges whose orientation is not recovered, under single-variable random interv…
Deriving Causal Order from Single-Variable Interventions: Guarantees & Algorithm
Mathieu Chevalley, Patrick Schwab, Arash Mehrjou
Targeted and uniform interventions to a system are crucial for unveiling causal relationships. While several methods have been developed to leverage interventional data for causal…
The CausalBench challenge: A machine learning contest for gene network inference from single-cell perturbation data
Mathieu Chevalley, Jacob Sackett-Sanders, Yusuf Roohani +15
In drug discovery, mapping interactions between genes within cellular systems is a crucial early step. Such maps are not only foundational for understanding the molecular mechanism…
In-silico biological discovery with large perturbation models
Djordje Miladinovic, Tobias Höppe, Mathieu Chevalley +6
Data generated in perturbation experiments link perturbations to the changes they elicit and therefore contain information relevant to numerous biological discovery tasks -- from u…
Multi-megabase scale genome interpretation with genetic language models
Frederik Träuble, Lachlan Stuart, Andreas Georgiou +9
Understanding how molecular changes caused by genetic variation drive disease risk is crucial for deciphering disease mechanisms. However, interpreting genome sequences is challeng…