3 citations · 5 across the 4 of their papers we have counts for
5 papers
Multi-center Medical Data Mining with FL-Net - A One-stop Shop for Federated Learning
Simon Süwer, Julian Klemm, Elisa Acitelli +39
Federated learning enables collaborative training without sharing patient-level data, but most studies remain simulations. Based on five requirements derived from the literature, w…
Batch effects can impair federated learning in multi-center omics studies
Yuliya Burankova, Julian Klemm, Jens J. G. Lohmann +5
Federated learning (FL) enables collaborative analysis of biomedical data without exchanging sensitive patient-level information, but its performance in multi-center studies may be…
Privacy-preserving federated prediction of pain intensity change based on multi-center survey data
Supratim Das, Mahdie Rafie, Paula Kammer +9
Background: Patient-reported survey data are used to train prognostic models aimed at improving healthcare. However, such data are typically available multi-centric and, for privac…
UnPaSt: unsupervised patient stratification by biclustering of omics data
Michael Hartung, Andreas Maier, Yuliya Burankova +25
Unsupervised patient stratification is essential for disease subtype discovery, yet, despite growing evidence of molecular heterogeneity of non-oncological diseases, popular method…
Democratising Knowledge Representation with BioCypher
Sebastian Lobentanzer, Patrick Aloy, Jan Baumbach +26
Standardising the representation of biomedical knowledge among all researchers is an insurmountable task, hindering the effectiveness of many computational methods. To facilitate h…