26 citations · 36 across the 12 of their papers we have counts for
12 papers
Natural-Language-Guided Generator-Agnostic Shortlisting for Protein Binder Design
Gyubok Lee, Kiwoong Yoo, Jimin Seo +3
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…
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…
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…
Rediscovery of CNN's Versatility for Text-based Encoding of Raw Electronic Health Records
Eunbyeol Cho, Min Jae Lee, Kyunghoon Hur +3
Making the most use of abundant information in electronic health records (EHR) is rapidly becoming an important topic in the medical domain. Recent work presented a promising frame…
Universal EHR Federated Learning Framework
Junu Kim, Kyunghoon Hur, Seongjun Yang +1
Federated learning (FL) is the most practical multi-source learning method for electronic healthcare records (EHR). Despite its guarantee of privacy protection, the wide applicatio…
UniHPF : Universal Healthcare Predictive Framework with Zero Domain Knowledge
Kyunghoon Hur, Jungwoo Oh, Junu Kim +6
Despite the abundance of Electronic Healthcare Records (EHR), its heterogeneity restricts the utilization of medical data in building predictive models. To address this challenge,…
GenHPF: General Healthcare Predictive Framework with Multi-task Multi-source Learning
Kyunghoon Hur, Jungwoo Oh, Junu Kim +7
Despite the remarkable progress in the development of predictive models for healthcare, applying these algorithms on a large scale has been challenging. Algorithms trained on a par…
Unifying Heterogeneous Electronic Health Records Systems via Text-Based Code Embedding
Kyunghoon Hur, Jiyoung Lee, Jungwoo Oh +3
EHR systems lack a unified code system forrepresenting medical concepts, which acts asa barrier for the deployment of deep learningmodels in large scale to multiple clinics and hos…
Unifying Heterogeneous Electronic Health Records Systems via Text-Based Code Embedding
Kyunghoon Hur, Jiyoung Lee, Jungwoo Oh +3
Substantial increase in the use of Electronic Health Records (EHRs) has opened new frontiers for predictive healthcare. However, while EHR systems are nearly ubiquitous, they lack…