110 citations · 119 across the 7 of their papers we have counts for
7 papers
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…
Privacy of federated QR decomposition using additive secure multiparty computation
Anne Hartebrodt, Richard Röttger
Federated learning (FL) is a privacy-aware data mining strategy keeping the private data on the owners' machine and thereby confidential. The clients compute local models and send…
Federated singular value decomposition for high dimensional data
Anne Hartebrodt, Richard Röttger, David B. Blumenthal
Federated learning (FL) is emerging as a privacy-aware alternative to classical cloud-based machine learning. In FL, the sensitive data remains in data silos and only aggregated pa…
The FeatureCloud AI Store for Federated Learning in Biomedicine and Beyond
Julian Matschinske, Julian Späth, Reza Nasirigerdeh +29
Machine Learning (ML) and Artificial Intelligence (AI) have shown promising results in many areas and are driven by the increasing amount of available data. However, this data is o…
Privacy-preserving Artificial Intelligence Techniques in Biomedicine
Reihaneh Torkzadehmahani, Reza Nasirigerdeh, David B. Blumenthal +22
Artificial intelligence (AI) has been successfully applied in numerous scientific domains. In biomedicine, AI has already shown tremendous potential, e.g. in the interpretation of…