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NoisyFlow: differentially private optimal transport using neural networks for secure biomedical data sharing across multiple institutions.

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Bibliographic Details
Title:	NoisyFlow: differentially private optimal transport using neural networks for secure biomedical data sharing across multiple institutions.
Authors:	Li Y; Department of Computer Science, Yale University, New Haven, CT 06511, United States., Khandekar N; Department of Computer Science, Yale University, New Haven, CT 06511, United States., Wang S; Department of Computer Science, Yale University, New Haven, CT 06511, United States., Khanna V; Department of Biostatistics, Yale School of Public Health, New Haven, CT 06511, United States., Sanker J; Department of Computer Science, Yale University, New Haven, CT 06511, United States., Gerstein MB; Department of Computer Science, Yale University, New Haven, CT 06511, United States.; Program in Computational Biology and Biomedical Informatics, Yale University, New Haven, CT 06511, United States.; Department of Statistics and Data Science, Yale University, New Haven, CT 06511, United States.
Source:	Bioinformatics (Oxford, England) [Bioinformatics] 2026 Jul 01; Vol. 42 (Supplement_1).
Publication Type:	Journal Article
Journal Info:	Publisher: Oxford University Press Country of Publication: England NLM ID: 9808944 Publication Model: Print Cited Medium: Internet ISSN: 1367-4811 (Electronic) Linking ISSN: 13674803 NLM ISO Abbreviation: Bioinformatics Subsets: MEDLINE
Database:	MEDLINE Ultimate

Description
ISSN:	1367-4811
DOI:	10.1093/bioinformatics/btag239