Author

Youngmok Jung

Korea Advanced Institute of Science and TechnologyORCID 0000-0002-2613-1442Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Assay:whole-genome sequencing· 2 Research Focus:benchmarking· 1 Approach:deep learning· 1 Approach:machine learning (classical)· 1 Approach:statistical model· 1 Research Focus:variant calling· 1

Frequent Co-Authors

Dongsu HaniD· 2 shared Hwijoon LimiD· 1 shared Jeong Seok LeeiD· 1 shared Jinwoo ParkiD· 1 shared Young Seok JuiD· 1 shared

Activity by Year

2022-2023 · peak 1/yr

Co-Author Collaboration Network

5 top collaborators · 10 mutual co-author links

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2 shared papersBioinformatics

Dongsu HanORCID

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2 papers

Recent·Most cited

BioinformaticsbioRxivPreprintAug 12, 2023

Generalizing deep variant callers via domain adaptation and semi-supervised learning

Youngmok Jung, Jinwoo Park, Hwijoon Lim…·Dongsu Han

This paper introduces RUN-DVC, a semi-supervised training method for deep variant callers that improves generalization across sequencing methods with varying error profiles.

DOI 10.1101/2023.08.12.549820

whole-genome sequencingdeep learning

𝕏 PostBluesky

BioinformaticsBioinformaticsPeer-reviewedMar 3, 2022

BWA-MEME: BWA-MEM emulated with a machine learning approach

Youngmok Jung, Dongsu Han

BWA-MEME improves short-read alignment efficiency by using a machine learning approach to reduce computational overhead while maintaining accuracy.

340 citationstop 1%

whole-genome sequencingmachine learning (classical)

𝕏 PostBluesky