A transfer learning framework to elucidate the clinical relevance of altered proximal tubule cell states in kidney disease

A transfer learning framework to elucidate the clinical relevance of altered proximal tubule cell states in kidney disease

Summary: The application of single-cell technologies in clinical nephrology remains elusive. We generated an atlas of transcriptionally defined cell types and cell states of human kidney disease by integrating single-cell signatures reported in the literature with newly generated signatures obtained...

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Bibliographic Details
Main Authors: David Legouis, Anna Rinaldi, Daniele Malpetti, Gregoire Arnoux, Thomas Verissimo, Anna Faivre, Francesca Mangili, Andrea Rinaldi, Lorenzo Ruinelli, Jerome Pugin, Solange Moll, Luca Clivio, Marco Bolis, Sophie de Seigneux, Laura Azzimonti, Pietro E. Cippà
Format: Article
Language:English
Published: Elsevier 2024-03-01
Series:iScience
Subjects:
Cell biology
Integrative aspects of cell biology
Transcriptomics
Machine learning
Online Access:http://www.sciencedirect.com/science/article/pii/S2589004224004929

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http://www.sciencedirect.com/science/article/pii/S2589004224004929

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