Author

Y. R. Chung

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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Pathology· 2 Immunology· 1 Microbiology· 1

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 3 Approach:deep learning· 2 Research Focus:differential expression· 2 Approach:experimental assay· 2 Assay:imaging· 2 Organism:bacteria (other)· 1 Assay:bulk RNA-seq· 1 Research Focus:cancer & tumor microenvironment· 1

Frequent Co-Authors

J. Park· 2 shared A. Khan· 1 shared A. Nose· 1 shared B. Awakoaiye· 1 shared B. H. Kang· 1 shared D.-I. Kim· 1 shared E. J. Chung· 1 shared H. Park· 1 shared

Activity by Year

2020-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 14 mutual co-author links

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2 shared papersPathology

J. Park

Mutual co-authors in cluster

Author profile →

4 papers

Recent·Most cited

Replication-attenuated r3LCMV vectors potentiate tumor control via IFN-I

Y. R. Chung, B. Awakoaiye, T. Dangi, S. Fourati, P. Penaloza-MacMaster

This study shows that replication-attenuated r3LCMV vectors enhance tumor control and survival in multiple tumor models, including immunodeficient mice, through IFN-I signaling.

bioRxiv · Dec 8, 2023 · PreprintImmunology

Unusual Morphological Changes of Rugositalea oryzae, A Novel Wrinkled Bacterium Isolated from The Rice Rhizosphere, Under Nutrient Stress

Y. R. Chung, J.-E. Lee, Z. Aslam, E. J. Chung, A. Khan, K. H. Lee, B. H. Kang, S. Niraula, W.-S. Chang

The paper describes a novel bacterial strain, Rugositalea oryzae, isolated from rice rhizosphere that exhibits morphological changes under nutrient stress.

bioRxiv · Aug 29, 2022 · PreprintMicrobiology

Comparative analysis of high- and low-level deep learning approaches in microsatellite instability prediction

J. Park, Y. R. Chung, A. Nose

This study compares high- and low-level deep learning approaches for microsatellite instability prediction using TCGA data, finding the high-level approach superior and identifying key morphological features.

bioRxiv · Apr 9, 2022Pathology

Aggregation of Cohorts for Histopathological Diagnosis with Deep Morphological Analysis

J. Park, Y. R. Chung, S. T. Kong, Y. W. Kim, H. Park, K. Kim, D.-I. Kim, K.-H. Jung

This paper introduces metrics to group pathology cohorts for deep learning cancer diagnosis, balancing model count and performance without linearly increasing models with cohort numbers.

bioRxiv · Oct 13, 2020Pathology