6 papers
Natural-Language-Guided Generator-Agnostic Shortlisting for Protein Binder Design
Gyubok Lee, Kiwoong Yoo, Jimin Seo +2
Modern de novo design workflows generate many candidate protein binders, but wet-lab validation capacity remains limited, making shortlisting a major bottleneck. We study whether L…
Leveraging Biokinetic Knowledge Priors for Data-Scarce Bioprocess Modeling
Kyunghoon Hur, Eunjung Jeon, Hyun Woo Kim +2
While deep learning has accelerated drug discovery, its impact on biomanufacturing has been considerably more limited. The reason is data scarcity. Bioreactor experiments are high-…
Compressing the Validation Bottleneck: An Agentic Self-Driving Lab for Scientific Discovery
Kyunghoon Hur, Chihun Lee
Agentic AI-for-Science can automate ideation, planning, and analysis, but final validation still depends on real experiments. A self-driving lab (SDL) can execute those experiments…
Multi-lingual Multi-institutional Electronic Health Record based Predictive Model
Kyunghoon Hur, Heeyoung Kwak, Jinsu Jang +2
Large-scale EHR prediction across institutions is hindered by substantial heterogeneity in schemas and code systems. Although Common Data Models (CDMs) can standardize records for…
Federated Learning for Heterogeneous Electronic Health Record Systems with Cost Effective Participant Selection
Jiyoun Kim, Junu Kim, Kyunghoon Hur +1
The increasing volume of electronic health records (EHRs) presents the opportunity to improve the accuracy and robustness of models in clinical prediction tasks. Unlike traditional…
MD-ViSCo: A Unified Model for Multi-Directional Vital Sign Waveform Conversion
Franck Meyer, Kyunghoon Hur, Edward Choi
Despite the remarkable progress of deep-learning methods generating a target vital sign waveform from a source vital sign waveform, most existing models are designed exclusively fo…