Ervin Tasnádi

mulea - an R package for enrichment analysis using multiple ontologies and empirical FDR correction

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

Automatic deep learning driven label-free image guided patch clamp system for human and rodent in vitro slice physiology

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

A deep learning framework for nucleus segmentation using image style transfer

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: an open-source active surface tool for single cell segmentation in 3D 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.