Author

Jungwha Julia Lee

Northwestern UniversityORCID 0000-0002-0806-0847Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Organism:human· 2 Assay:bulk RNA-seq· 1 Research Focus:clustering· 1 Approach:machine learning (classical)· 1 Approach:statistical model· 1 Research Focus:variant calling· 1 Assay:whole-exome sequencing· 1

Frequent Co-Authors

Adam B. OlsheniD· 1 shared Alan Hunter ShainiD· 1 shared Annika L. MartyiD· 1 shared Aravind K. BandariiD· 1 shared Bishal TandukariD· 1 shared Brent D. Wilson· 1 shared Chiang‐Ching HuangiD· 1 shared Darwin ChangiD· 1 shared

Activity by Year

2017-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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1 shared paperGenomics

Adam B. OlshenORCID

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2 papers

Recent·Most cited

Molecular effects of indoor tanning

Pedram Gerami, Bishal Tandukar, Delahny Deivendran, Shantel Olivares, Limin Chen, Tuyet M. Tan, Tuyet Tan, Harsh Sharma, Aravind K. Bandari, Noel Cruz‐Pacheco, Darwin Chang, Annika L. Marty, Adam B. Olshen, Natália Faraj Murad, Jing Song, , Alan Hunter Shain

This study examines the molecular effects of indoor tanning by analyzing mutation patterns in melanocytes from tanning bed users and finds increased mutation burdens and distinct mutational signatures.

Science Advances · Dec 12, 2025Peer-reviewedGenomics

Machine Learning of the Cardiac Phenome and Skin Transcriptome to Categorize Heart Disease in Systemic Sclerosis

Monique Hinchcliff, Tracy Frech, Tammara A. Wood, Chiang‐Ching Huang, Jungwha Julia Lee, Kathleen Aren, John Jake Ryan, Brent D. Wilson, Lauren Beussink‐Nelson, Michael L. Whitfield, Rahul C. Deo, Sanjiv Jayendra Shah

This paper uses machine learning to classify heart disease subgroups in systemic sclerosis using echocardiographic and skin transcriptome data, identifying distinct phenotypes and associated molecular pathways.

bioRxiv · Nov 3, 2017 · PreprintGenomics