Author

Tracy Frech

Johns Hopkins UniversityORCID 0000-0002-5472-3840Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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

Frequent Co-Authors

Kathleen AreniD· 2 shared Monique HinchcliffiD· 2 shared Alex Shaeffer· 1 shared Anh H Chung· 1 shared Brent D. Wilson· 1 shared Carla Marie CudaiD· 1 shared Chiang‐Ching HuangiD· 1 shared Deborah R. WinteriD· 1 shared

Activity by Year

2017-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 33 mutual co-author links

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2 shared papersGenomics

Kathleen ArenORCID

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2 papers

Recent·Most cited

Three Distinct Transcriptional Profiles of Monocytes Correlate with Disease Activity in SSc Patients

Hadijat M. Makinde, Julia L.M. Dunn, Gaurav T. Gadhvi, Mary A. Carns, Kathleen Aren, Anh H Chung, Lutfiyya N. Muhammad, Jing Song, Carla Marie Cuda, Salina T. Dominguez, John Erik Pandolfino, Jane E. Dematte D’Amico, G. R. Scott Budinger, Shervin Assassi, Tracy Frech, , Deborah R. Winter

This study identifies three monocyte transcriptional subgroups in SSc patients that correlate with disease activity, particularly lung function.

bioRxiv · Jan 29, 2022 · PreprintGenomics

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