2 citations · 2 across the 6 of their papers we have counts for
6 papers
PACER: Acyclic Causal Discovery from Large-Scale Interventional Data
Ramon Viñas Torné, Sílvia Fàbregas 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…
HiRef: Leveraging Hierarchical Ontology and Network Refinement for Robust Medication Recommendation
Yan Ting Chok, Soyon Park, Seungheun Baek +3
Medication recommendation is a crucial task for assisting physicians in making timely decisions from longitudinal patient medical records. However, real-world EHR data present sign…
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…
MolPLA: A Molecular Pretraining Framework for Learning Cores, R-Groups and their Linker Joints
Mogan Gim, Jueon Park, Soyon Park +5
Molecular core structures and R-groups are essential concepts in drug development. Integration of these concepts with conventional graph pre-training approaches can promote deeper…