Author

Juyeon Park

Korea Advanced Institute of Science and TechnologyPrimary ORCID profile (0000-0001-9549-4645 · 1 paper)Unclaimed ORCID profile · follow to get notified of new preprints

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Same name, identified by ORCID

Juyeon ParkiD1 paper · Scripps Research Institute

Biology Disciplines

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Bioinformatics· 1 Biophysics· 1 Cell Biology· 1 Immunology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 3 Research Focus:cell type annotation· 2 Organism:human· 2 Assay:imaging· 2 Organism:bacteria (other)· 1 Approach:contrastive learning· 1 Research Focus:drug response / drug discovery· 1 Approach:experimental assay· 1

Frequent Co-Authors

YongKeun ParkiD· 2 shared Aashish BhattiD· 1 shared Avinash YaganapuiD· 1 shared Bon‐Kyoung KooiD· 1 shared ChulMin Oh· 1 shared Dalton Towers· 1 shared Garnett H. Kelsoe· 1 shared Geon KimiD· 1 shared

Activity by Year

2025-2026 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 11 mutual co-author links

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2 shared papersBiophysics

YongKeun ParkORCID

Mutual co-authors in cluster

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4 papers

Recent·Most cited

Genetically restricted human antibodies recognize a conserved β-hairpin in postfusion influenza hemagglutinin

Joel Finney, Juyeon Park, Martin Dodes Traian, Masayuki Kuraoka, Dalton Towers, M. Anthony Moody, George Georgiou, Garnett H. Kelsoe, Stephen Harrison

The paper identifies genetically restricted human antibodies that recognize a conserved β-hairpin in postfusion influenza HA and shows that specific IGHV1-2 alleles are required for their production.

bioRxiv · Nov 3, 2026 · PreprintImmunology

Label-Free 4D Holotomography with Depth-Adaptive Segmentation for Quantitative Analysis of Lipid Droplet Dynamics in Hepatic Organoids

Jimin Cho, Hoyeon Lee, ChulMin Oh, Juyeon Park, YongKeun Park, Bon‐Kyoung Koo

This paper develops a label-free 3D holotomography method to track lipid droplet dynamics in hepatic organoids under different fatty-acid treatments, revealing distinct accumulation strategies.

Biophotonics discovery. · Sep 30, 2026Peer-reviewedBiophysics

Prediction of bacterial protein-compound interactions with only positive samples

Ki‐Hwa Kim, Avinash Yaganapu, Sai Kosaraju, Aashish Bhatt, Yun Luo, Sai Phani Parsa, Juyeon Park, Hyun Lee, Jun Hyuck Lee, Tae‐Jin Oh, M. Kang

This paper introduces BIN-PU, a PU learning framework for predicting bacterial protein-compound interactions using only positive samples, showing improved performance over existing methods.

Bioinformatics · Feb 6, 2026Peer-reviewedBioinformatics

Label-free classification of cell death pathways via holotomography-based deep learning framework

Minwook Kim, W. Park, Geon Kim, Sejoon Oh, Jaephil Do, Juyeon Park, YongKeun Park

This paper develops a label-free deep learning framework using holotomography to classify cell death pathways with high accuracy.

bioRxiv · Jun 6, 2025 · PreprintCell Biology