Author

Jae‐Yoon Kim

Korea Research Institute of Bioscience and BiotechnologyORCID 0000-0002-8557-0998Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2 Bioengineering· 1

Signature Assays, Organisms & Methods

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Research Focus:variant effect prediction· 2 Research Focus:benchmarking· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Organism:E. coli· 1 Approach:experimental assay· 1 Research Focus:gene regulatory network inference· 1 Assay:GWAS summary statistics· 1

Frequent Co-Authors

Ahmed KabiliD· 1 shared I-Chih KuoiD· 1 shared Jessica Xin· 1 shared Kai-Wei Chang· 1 shared Kelly Marshall McNagnyiD· 1 shared Namshin KimiD· 1 shared Nika ShakibaiD· 1 shared Seon‐Young KimiD· 1 shared

Activity by Year

2019-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 17 mutual co-author links

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1 shared paperBioengineering

Ahmed KabilORCID

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3 papers

Recent·Most cited

BioengineeringSTAR ProtocolsPeer-reviewedJul 9, 2026

Cost-Effective Purification of Endotoxin-Free LIF, IL-2, and IL-33

Jae‐Yoon Kim, Jessica Xin, I-Chih Kuo…·Yanpu He

The paper describes a cost-effective E. coli-based method for producing endotoxin-free cytokines, demonstrating bioactivity comparable to commercial products at 5%-10% of the cost.

DOI 10.1016/j.xpro.2026.104698

E. coliproteomics

𝕏 PostBluesky

BioinformaticsbioRxivPreprintMar 11, 2026

HAETAE: A highly accurate and efficient epigenome transformer for tissue-specific histone modification prediction

Seung‐Jin Park, Soo-Hyun Im, Jae‐Yoon Kim, Seon‐Young Kim

HAETAE predicts tissue-specific histone modifications with high accuracy using a 5-base framework and long-read sequencing data.

DOI 10.64898/2026.03.09.708388

long-read sequencingdeep learning

𝕏 PostBluesky

BioinformaticsbioRxivPreprintSep 5, 2019

CVRMS: Cross-validated Rank-based Marker Selection for Genome-wide Prediction of Low Heritability

Seongmun Jeong, Jae‐Yoon Kim, Namshin Kim

CVRMS selects optimal biomarkers for genome-wide prediction using ridge regression, improving phenotype predictability across diverse datasets.

DOI 10.1101/756130

humanplant (other)

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