6 papers · 1 filter
Feature Space Selection and Heterogeneous Effect Estimation for Blood-Brain Barrier Permeability: A Random Forest to the Generalized Random Forest Pipeline
Tshemollo Rapolai, Seite Makgai, Mohammad Arashi
Predicting blood-brain barrier (BBB) permeability is critical for central nervous system drug discovery. Using the MoleculeNet BBBP dataset (n = 2039), this study systematically ab…
Nonparanormal Bayesian Learning of Directed Acyclic Graphs under Gamma and Inverse-Gamma Innovation Priors: Closed-Form Scores and Informed Sampling
Samaneh Nazari, Mohammad Arashi
Bayesian structure learning for directed acyclic graphs (DAGs) is a central tool for reconstructing biological networks, yet it often assumes the data are jointly Gaussian. In moti…
Semiparametric Bayesian structure learning of nonparanormal directed acyclic graphs with local--global shrinkage
Samaneh Nazari, Mohammad Arashi
Bayesian structure learning of directed acyclic graphs (DAGs) is central to high-dimensional causal discovery, yet existing methods mostly assume multivariate Gaussian data, which…
Variational Bayesian Sparse Negative Binomial Regression
Mitra Kharabati, Morteza Amini, Mohammad Arashi
Count data with overdispersion and high-dimensional predictors pose significant challenges in modern applications. While negative binomial regression offers a flexible modeling fra…
Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks
Samaneh Nazari, Mohammad Arashi, Abdolnasser Sadeghkhani
Structure learning of directed acyclic graphs (DAGs) from observational data is a foundational task in causal discovery and is widely used to infer regulatory networks from medical…
Bayesian DAG Structure Learning with Simultaneous Shrinkage Covariance Estimation under Scale-Mixture Error Distributions in the Proportional High-Dimensional Regime
Samaneh Nazari, Mohammad Arashi, Abdolnasser Sadeghkhani
We propose a unified Bayesian framework namely robust DAG-Cholesky horseshoe (R-DACH) for joint directed acyclic graph (DAG) structure learning and precision matrix estimation in t…