Author

Guray Kuzu

ORCID 0009-0006-5306-8705Unclaimed 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:differential expression· 2 Approach:experimental assay· 2 Organism:human· 2 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1 Research Focus:cell-cell communication· 1 Approach:deep learning· 1

Frequent Co-Authors

Frederick J. KingiD· 2 shared Andrea Grosso· 1 shared Andrew S. HaganiD· 1 shared Bao Nguyen· 1 shared Bin FangiD· 1 shared Bin FangiD· 1 shared Brandon L. Taylor· 1 shared Casey J. N. MathisoniD· 1 shared

Activity by Year

2024-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 23 mutual co-author links

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2 shared papersCell Biology

Frederick J. KingORCID

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3 papers

Recent·Most cited

Cell BiologybioRxivPreprintSep 24, 2026

LNA043 and ANGPTL3 Interactions with Integrin α5β1 and Fibronectin Drive Distinct Stromal and Immune Responses

Andrea Grosso, Stefano Cucuzza, Elsa Gambs…·Mara Fornaro

The paper investigates how LNA043 and ANGPTL3 interact with integrin α5β1 and fibronectin to drive distinct stromal and immune responses in osteoarthritis.

DOI 10.64898/2026.09.14.751366

humanexperimental assay

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Cell BiologyCommunications BiologyPeer-reviewedJul 9, 2025

AHR activation accelerates the resolution of TGF-β1 induced fibroblast activation and promotes alveolar type 1 cell regeneration in alveolar organoids

Andrew S. Hagan, Scott Williams, Casey J. N. Mathison…·Rebekah S. Decker

The paper identifies DB-11-BE87 as a compound that promotes AT1 cell regeneration by reducing TGF-β1-induced fibroblast activation through AHR activation in alveolar organoids.

12 citationstop 2%

humanscRNA-seq

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BioinformaticsJournal of Chemical Information and ModelingPeer-reviewedJan 31, 2024

Compound activity prediction with dose-dependent transcriptomic profiles and deep learning

William J. Godinez, Vladimir Trifonov, Bin Fang…·Peter Skewes-Cox

The paper introduces TAT models to predict compound activity using dose-dependent transcriptomic profiles, achieving 51% assay prediction success and validating malaria inhibitor identification.

7 citations

bulk RNA-seqdeep learning

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