Author

S. Hyun

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioengineering· 1 Cancer Biology· 1 Microbiology· 1

Signature Assays, Organisms & Methods

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Approach:Bayesian inference· 1 Assay:bulk RNA-seq· 1 Research Focus:cancer & tumor microenvironment· 1 Research Focus:cell signaling & trafficking· 1 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Approach:contrastive learning· 1 Approach:deep learning· 1

Frequent Co-Authors

B. Park· 1 shared D.-S. Suh· 1 shared E. Shin· 1 shared E. V. Armbrust· 1 shared F. Ribalet· 1 shared G. L. Britten· 1 shared H. Jin· 1 shared H. Jung· 1 shared

Activity by Year

2020-2021 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 14 mutual co-author links

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1 shared paperBioengineering

B. Park

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Author profile →

3 papers

Recent·Most cited

A flexible Bayesian approach to estimating size-structured matrix population models

J. P. Mattern, K. Glauninger, G. L. Britten, J. Casey, S. Hyun, Z. Wu, E. V. Armbrust, Z. Harchaoui, F. Ribalet

This paper introduces a Bayesian framework to improve the estimation of size-structured matrix population models for microbial dynamics, demonstrating better performance and biological interpretability.

bioRxiv · Jul 16, 2021 · PreprintMicrobiology

An alternative miRISC targeting a coding mutation site in FOXL2 links to granulosa cell tumor

J. Bae, E. Shin, H. Jin, D.-S. Suh, Y. Luo, H.-J. Ha, T. H. Kim, Y. Hahn, S. Hyun, K. Lee

The paper identifies a miRISC targeting a FOXL2 coding mutation in granulosa cell tumors, linking it to haploinsufficiency and tumor progression.

The EMBO Journal · Nov 20, 2020Peer-reviewedCancer Biology

Deep Metric Learning Network using Proxies for Chromosome Classification and Retrieval in Karyotyping Test

H. Jung, B. Park, S. Lee, S. Hyun, J. Lee, J. Seo, S. Koo, M. Lee

This paper introduces Proxy-ResNeXt-CBAM, a metric learning network for chromosome classification and retrieval in karyotyping, achieving high accuracy and effective clustering of embeddings.

bioRxiv · May 24, 2020 · PreprintBioengineering