Ervin Tasnádi

mulea provides an R package for enrichment analysis across multiple ontologies with empirical FDR correction to address limitations in traditional gene set enrichment methods.

The paper develops an automated patch clamp system using deep learning and image analysis to measure neurons in human and rodent brain slices.

The paper introduces a deep learning framework for nucleus segmentation that uses image style transfer to adapt to new data without expert annotations, outperforming existing methods on diverse microscopy images.

3D-Cell-Annotator provides a tool for automated 3D cell segmentation in microscopy images using active surfaces and shape descriptors, achieving expert-level precision.