Author

Tae Lyun Ko

Korea Institute of Science & Technology Information

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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Bioinformatics· 4

Signature Assays, Organisms & Methods

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Organism:human· 3 Assay:scRNA-seq· 3 Research Focus:differential expression· 2 Approach:statistical model· 2 Research Focus:cell type annotation· 1 Research Focus:database / resource· 1 Research Focus:deconvolution· 1 Research Focus:drug response / drug discovery· 1

Frequent Co-Authors

Hyojung PaikiD· 4 shared Sung Ki LeeiD· 3 shared Hyoung‐Sam Heo· 2 shared Marina SirotaiD· 2 shared Boris OskotskyiD· 1 shared Byung Soo LeeiD· 1 shared Daewon LeeiD· 1 shared Daniel G. BunisiD· 1 shared

Activity by Year

2026-2026 · peak 4/yr

Co-Author Collaboration Network

10 top collaborators · 26 mutual co-author links

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4 shared papersBioinformatics

Hyojung PaikORCID

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Author profile →

4 papers

Recent·Most cited

GENOAR: Global Engine for Navigating the Omnicell space via Agent-based Research

Hyojung Paik, Tae Lyun Ko, Dogyun Kim, Dongmin Shin, Marina Sirota, Boris Oskotsky, Tomiko Oskotsky, Taeyoung Lee, Hyeonjae Lee, Daewon Lee

GENOAR enables scalable, AI-compatible single-cell transcriptomic analysis of over 54 million cells from humans and mice, outperforming human experts in data retrieval and enabling new biological insights.

bioRxiv · Oct 21, 2026 · PreprintBioinformatics

GOLDEN: Gene expression and allele-informed decomposition for genetic heterogeneity in spatial transcriptomic data

Sangjeong Lee, Tae Lyun Ko, Hyoung‐Sam Heo, Junguee Lee, Sung Ki Lee, Hyojung Paik

GOLDEN deconvolves genetic heterogeneity in spatial transcriptomic data by integrating gene expression and allele information, achieving high accuracy in fraction estimation and classification.

bioRxiv · Oct 20, 2026 · PreprintBioinformatics

Machine learning-driven decoding of maternal immune signatures in repeated pregnancy loss

Tae Lyun Ko, Jaesub Park, Dongju Leem, Junho Kim, Jae Won Han, Jin Sol Park, Sung Ki Lee, Hyojung Paik

The paper identifies maternal immune signatures in repeated pregnancy loss using single-cell RNA sequencing and machine learning, linking them to potential drug repurposing candidates.

PLoS Computational Biology · Sep 17, 2026Peer-reviewedBioinformatics

Searching for influencers among placental immune cells in preeclampsia.

Hyojung Paik, Tae Lyun Ko, Myungsun Park, J Park, Daniel G. Bunis, Marina Sirota, Byung Soo Lee, Hyoung‐Sam Heo, Sung Ki Lee

The paper identifies SPP1 and CD44 as influencers of inflammation in preeclampsia by analyzing single-cell transcriptional profiles of placental immune cells.

BioData Mining · Apr 28, 2026Peer-reviewedBioinformatics