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Characterization and classification of chronic kidney disease by spatial MIST and deep learning algorithm

Year: 2025
Type: article
Abstract: In this study, we offer a novel spatial analysis of markers relevant to CKD, which may provide useful insights into disease progression. By using this spatial proximity data, we created a GNN model that is capable of classifying disease severity and identifying markers that are most important for its classification. This integrative approach offers a foundation for future studies aimed at developing clinically actionable tools for CKD diagnosis and prognosis.
DOI: 10.1152/ajprenal.00265.2025
Authors: Arafat Meah, Nehaben A. Gujarati, Vivette D. D’Agati, Monica P. Revelo, Sandeep K. Mallipattu, Jun Wang
Institutions: State University of New York, Stony Brook University, Stony Brook School, Stony Brook Medicine, Columbia University Irving Medical Center, Columbia University, University of Utah, Northport VA Medical Center
Language: English
Subfield: Nephrology
Field: Medicine
Open Access status: hybrid
Awards: G1820917308, G2315163377, G2918895993, G4120147668, G4130464018, G6340225893, G741185792
OpenAlex ID: W4415523826
FWCI: 0.618
Cites: 50
Cited by: 1
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