5 citations · 5 across the 2 of their papers we have counts for
6 papers
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…
HyFed: A Hybrid Federated Framework for Privacy-preserving Machine Learning
Reza Nasirigerdeh, Reihaneh Torkzadehmahani, Julian Matschinske +3
Federated learning (FL) enables multiple clients to jointly train a global model under the coordination of a central server. Although FL is a privacy-aware paradigm, where raw data…
Comparative transcriptome analysis reveals key epigenetic targets in SARS-CoV-2 infection
Marisol Salgado-Albarran, Erick I. Navarro-Delgado, Aylin Del Moral-Morales +4
COVID-19 is an infection caused by SARS-CoV-2 (Severe Acute Respiratory Syndrome coronavirus 2), which has caused a global outbreak. Current research efforts are focused on the und…
Federated Multi-Mini-Batch: An Efficient Training Approach to Federated Learning in Non-IID Environments
Reza Nasirigerdeh, Mohammad Bakhtiari, Reihaneh Torkzadehmahani +4
Federated learning has faced performance and network communication challenges, especially in the environments where the data is not independent and identically distributed (IID) ac…
TiCoNE 2: A Composite Clustering Model for Robust Cluster Analyses on Noisy Data
Christian Wiwie, Richard Röttger, Jan Baumbach
Identifying groups of similar objects using clustering approaches is one of the most frequently employed first steps in exploratory biomedical data analysis. Many clustering method…
Elucidation of time-dependent systems biology cell response patterns with time course network enrichment
Christian Wiwie, Alexander Rauch, Anders Haakonsson +5
Advances in OMICS technologies emerged both massive expression data sets and huge networks modelling the molecular interplay of genes, RNAs, proteins and metabolites. Network enric…