110 citations · 119 across the 7 of their papers we have counts for
3 papers · 1 filter
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…
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…
Privacy-Preserving Multi-Center Differential Protein Abundance Analysis with FedProt
Yuliya Burankova, Miriam Abele, Mohammad Bakhtiari +28
Quantitative mass spectrometry has revolutionized proteomics by enabling simultaneous quantification of thousands of proteins. Pooling patient-derived data from multiple institutio…