4 papers · 1 filter
PACER: Acyclic Causal Discovery from Large-Scale Interventional Data
Ramon Viñas Torné, SÃlvia Fà bregas Salazar, Soyon Park +4
Inferring the structure of directed acyclic graphs (DAGs) from data is a central challenge in causal discovery, particularly in modern high-dimensional settings where large-scale i…
CoTox: Chain-of-Thought-Based Molecular Toxicity Reasoning and Prediction
Jueon Park, Yein Park, Minju Song +4
Drug toxicity remains a major challenge in pharmaceutical development. Recent machine learning models have improved in silico toxicity prediction, but their reliance on annotated d…
GPO-VAE: Modeling Explainable Gene Perturbation Responses utilizing GRN-Aligned Parameter Optimization
Seungheun Baek, Soyon Park, Yan Ting Chok +2
Motivation: Predicting cellular responses to genetic perturbations is essential for understanding biological systems and developing targeted therapeutic strategies. While variation…
CRADLE-VAE: Enhancing Single-Cell Gene Perturbation Modeling with Counterfactual Reasoning-based Artifact Disentanglement
Seungheun Baek, Soyon Park, Yan Ting Chok +4
Predicting cellular responses to various perturbations is a critical focus in drug discovery and personalized therapeutics, with deep learning models playing a significant role in…