Author

Judith M. Chung

University of California, IrvineORCID 0000-0002-3256-1465Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Genetic & Genomic Medicine· 1 Microbiology· 1

Signature Assays, Organisms & Methods

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Organism:human· 2 Research Focus:differential expression· 1 Research Focus:GWAS / association mapping· 1 Assay:GWAS summary statistics· 1 Approach:machine learning (classical)· 1 Assay:metagenomics· 1 Approach:statistical model· 1

Frequent Co-Authors

David M. HaasiD· 2 shared Robert M. SilveriD· 2 shared Amy L MooreiD· 1 shared Anne‐Catrin UhlemanniD· 1 shared Aya Brown KaviD· 1 shared Brian M. Mercer· 1 shared Erin WattersiD· 1 shared George I. AustiniD· 1 shared

Activity by Year

2024-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 38 mutual co-author links

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2 shared papersGenetic & Genomic Medicine

David M. HaasORCID

Mutual co-authors in cluster

Author profile →

2 papers

Recent·Most cited

Maternal and offspring genome-wide association study of C-reactive protein reveals limited polygenic association with gestational diabetes mellitus

Yu Zhang, Amy L Moore, Kelli K. Ryckman, Qi Yan, Rafael F. Guerrero, Ming Li, Robert M. Silver, Lynn M. Yee, Uma M. Reddy, Maisa N. Feghali, Judith M. Chung, David M. Haas, Kok Lim Kua, Nianjun Liu

This study investigates the genetic associations of early pregnancy C-reactive protein (CRP) levels with gestational diabetes mellitus (GDM) using maternal and offspring genome-wide association studies (GWAS).

medRxiv · Sep 30, 2025 · PreprintGenetic & Genomic Medicine

Early prediction of preeclampsia using the first trimester vaginal microbiome

William F. Kindschuh, George I. Austin, Yoli Meydan, Heekuk Park, Julia A. Urban, Erin Watters, Susan V. Pollak, George Robert Saade, Judith M. Chung, Brian M. Mercer, William A. Grobman, David M. Haas, Robert M. Silver, Myrna G. Serrano, Gregory A. Buck, , Tal Korem

This study predicts preeclampsia using first trimester vaginal microbiome data with machine learning, achieving an auROC of 0.78 in the training set and 0.80 in an independent cohort.

bioRxiv · Dec 1, 2024 · PreprintMicrobiology