András Kriston

This study creates a high-resolution morpho-proteomic atlas of mitosis by decomposing it into 40 subsections and quantifying 4,350 proteins, revealing dynamic changes and links to cancer mutations.

This study uses AI-based image analysis and multi-omics profiling to link morphological features with molecular changes in ccRCC, identifying unique molecular profiles in aggressive rhabdoid tumors.

HCS-3DX is a new AI-driven system for 3D-oid high-content screening that improves resolution and single-cell analysis in 3D tumor models.

Pairing AI-driven image analysis with automated laser microdissection and sensitive mass spectrometry yields proteomes of single cells or nuclei chosen by their morphology, with tissue position retained.

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.

The paper shows that incorporating microenvironmental features improves machine learning-based single-cell phenotyping in imaging data.