Membership testing in markov equivalence classes via independence queries
Understanding causal relationships between variables is a fundamental problem with broad
impact in numerous scientific fields. While extensive research has been dedicated to\emph …
impact in numerous scientific fields. While extensive research has been dedicated to\emph …
Discovering gene regulatory networks of multiple phenotypic groups using dynamic Bayesian networks
P Suter, J Kuipers, N Beerenwinkel - Briefings in Bioinformatics, 2022 - academic.oup.com
Abstract Dynamic Bayesian networks (DBNs) can be used for the discovery of gene
regulatory networks (GRNs) from time series gene expression data. Here, we suggest a …
regulatory networks (GRNs) from time series gene expression data. Here, we suggest a …
Adaptivity complexity for causal graph discovery
D Choo, K Shiragur - Uncertainty in Artificial Intelligence, 2023 - proceedings.mlr.press
Causal discovery from interventional data is an important problem, where the task is to
design an interventional strategy that learns the hidden ground truth causal graph $ G (V, E) …
design an interventional strategy that learns the hidden ground truth causal graph $ G (V, E) …
New metrics and search algorithms for weighted causal DAGs
D Choo, K Shiragur - International Conference on Machine …, 2023 - proceedings.mlr.press
Recovering causal relationships from data is an important problem. Using observational
data, one can typically only recover causal graphs up to a Markov equivalence class and …
data, one can typically only recover causal graphs up to a Markov equivalence class and …
NetREm Network Regression Embeddings reveal cell-type transcription factor coordination for gene regulation
Motivation Transcription factor (TF) coordination plays a key role in gene regulation via
direct and/or indirect protein-protein interactions (PPIs) and DNA co-binding to regulatory …
direct and/or indirect protein-protein interactions (PPIs) and DNA co-binding to regulatory …
Using empirical biological knowledge to infer regulatory networks from multi-omics data
A Pačínková, V Popovici - BMC bioinformatics, 2022 - Springer
Background Integration of multi-omics data can provide a more complex view of the
biological system consisting of different interconnected molecular components, the crucial …
biological system consisting of different interconnected molecular components, the crucial …
Causal Discovery with Fewer Conditional Independence Tests
Many questions in science center around the fundamental problem of understanding causal
relationships. However, most constraint-based causal discovery algorithms, including the …
relationships. However, most constraint-based causal discovery algorithms, including the …
Learning massive interpretable gene regulatory networks of the human brain by merging Bayesian networks
N Bernaola, M Michiels, P Larrañaga… - PLOS Computational …, 2023 - journals.plos.org
We present the Fast Greedy Equivalence Search (FGES)-Merge, a new method for learning
the structure of gene regulatory networks via merging locally learned Bayesian networks …
the structure of gene regulatory networks via merging locally learned Bayesian networks …
Causal Discovery under Off-Target Interventions
Causal graph discovery is a significant problem with applications across various disciplines.
However, with observational data alone, the underlying causal graph can only be recovered …
However, with observational data alone, the underlying causal graph can only be recovered …
Membership Testing in Markov Equivalence Classes via Independence Query Oracles
Understanding causal relationships between variables is a fundamental problem with broad
impact in numerous scientific fields. While extensive research has been dedicated to …
impact in numerous scientific fields. While extensive research has been dedicated to …