Tomáš Majtner

This study uses in situ structural biology to reveal the molecular organization of chromatin at the nuclear periphery in human cells.

cryoTIGER uses deep learning to generate intermediate tilt images in cryo-electron tomography, improving 3D reconstruction and structural recovery.

This study evaluates tilt increments in cryo-ET to find the optimal balance between image quality and radiation damage, recommending 3 degrees as the best increment.

This study develops a deep learning method to automatically remove corrupted tilts in cryo-ET, showing high efficiency and reliability with trained models for direct application.

This paper introduces a high-confidence 3D template matching method for CryoET, enabling accurate localization of cellular structures with improved statistical confidence.

ENNGene is a tool that simplifies deep learning model building for genomics data, achieving state-of-the-art results on the RBP24 dataset by incorporating evolutionary conservation and custom network tuning.