Author

Taylor G. Eggertsen

University of VirginiaORCID 0000-0002-2149-9686Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Assay:bulk RNA-seq· 2 Organism:human· 2 Approach:benchmark study· 1 Research Focus:cell type annotation· 1 Research Focus:cell-cell communication· 1 Approach:deep learning· 1 Research Focus:differential expression· 1

Frequent Co-Authors

Jeffrey J. SaucermaniD· 3 shared Angela C. ZeigleriD· 1 shared Anne I. SperlingiD· 1 shared Catherine A. BonhamiD· 1 shared Catherine SanoiD· 1 shared Daniel AbebayehuiD· 1 shared Darius Hossainian· 1 shared David J. CsordasiD· 1 shared

Activity by Year

2022-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 30 mutual co-author links

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3 shared papersSystems Biology

Jeffrey J. SaucermanORCID

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Author profile →

3 papers

Recent·Most cited

Computational Analysis of Fibroblast Subpopulation Dynamics as a Driver of Fibrotic Foci Formation

Julie Leonard‐Duke, David J. Csordas, Riley T. Hannan, Catherine Sano, Darius Hossainian, Mariska Batavia, Robert Andrews, Mariapaola Ambrosone, Taylor G. Eggertsen, Tania E. Velez, Jeffrey M. Sturek, Anne I. Sperling, Daniel Abebayehu, Thomas Harrison Barker, Catherine A. Bonham, , SHAYN M. PEIRCE

This paper develops a multiscale computational model to study how Thy-1 expression in fibroblast subpopulations influences fibrotic foci formation in IPF.

bioRxiv · Aug 5, 2026 · PreprintBioengineering

Logic-based machine learning predicts how escitalopram attenuates cardiomyocyte hypertrophy

Taylor G. Eggertsen, Joshua G. Travers, Elizabeth J. Hardy, Matthew J. Wolf, Timothy A. McKinsey, Jeffrey J. Saucerman

The paper uses a logic-based machine learning method to predict how escitalopram reduces cardiomyocyte hypertrophy, validated through experiments and patient data.

Proceedings of the National Academy of Sciences · Mar 4, 2025Peer-reviewedSystems Biology

Benchmarking of protein interaction databases for integration with manually reconstructed signaling network models

Matthew W. Van de Graaf, Taylor G. Eggertsen, Angela C. Zeigler, Philip M. Tan, Jeffrey J. Saucerman

The paper benchmarks protein interaction databases for their ability to recover manually curated edges from signaling network models of cardiac hypertrophy, mechano-signaling, and fibrosis.

bioRxiv · Oct 25, 2022 · PreprintSystems Biology