Federated unsupervised random forest for privacy-preserving patient stratification

Motivation In the realm of precision medicine, effective patient stratification and disease subtyping demand innovative methodologies tailored for multi-omics data. Clustering techniques applied to multi-omics data have become instrumental in identifying distinct subgroups of patients, enabling a fi...

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Bibliographic Details
Published in:	Bioinformatics (Oxford, England) Vol. 40; no. Supplement_2; pp. ii198 - ii207
Main Authors:	Pfeifer, Bastian, Sirocchi, Christel, Bloice, Marcus D, Kreuzthaler, Markus, Urschler, Martin
Format:	Journal Article
Language:	English
Published:	England Oxford University Press 01.09.2024 Oxford Publishing Limited (England)
Subjects:	Algorithms Availability Big Data Biological analysis Cancer Cluster Analysis Clustering Computation Datasets Digital Health Humans Learning algorithms Machine Learning Neoplasms Patients Precision medicine Precision Medicine - methods Privacy Random Forest Subgroups Unsupervised Machine Learning
ISSN:	1367-4803, 1367-4811, 1367-4811
Online Access:	Get full text
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