Jan Ewald

AUTOENCODIX provides a standardized framework for training and evaluating autoencoders, demonstrating its utility across multi-modal biological data and offering insights into representation learning trade-offs.

This paper reviews 86 studies on biologically informed neural networks for multi-omics integration, highlighting trends and challenges in terminology, tools, and evaluation methods.

This paper shows that using the Segment Anything Model (SAM) for zero-shot confluence estimation in mesenchymal stem cells is effective without needing extensive dataset curation or fine-tuning.

This study uses dynamic optimization to analyze host-pathogen interactions in invasive aspergillosis, identifying key roles for epithelial cells and neutrophils in immune response and fungal virulence.

The paper identifies toxic intermediates in fungal metabolic pathways as potential antifungal targets using a random forest model and provides a web-service for exploration.