Ede Migh

Revamped transposon-induced hepatocellular carcinoma model reveals deep similarities of cancer-driver landscapes across mice and humans

The paper develops a new transposon-induced model to identify cancer drivers in mice and shows similarities in driver landscapes between mice and humans.

Prevalent Gut Phages Encode Modular Adhesins Mediating Epithelial Binding and Endoplasmic Reticulum Trafficking

The paper identifies gut phages with adhesin proteins that bind epithelial cells and traffic to the endoplasmic reticulum, revealing potential interactions with the human virome.

Intrahepatic reporter assay reveals leaky somatic blockade of L1 retrotransposition in mice

The paper demonstrates somatic L1 retrotransposition in adult mouse liver using an intrahepatic reporter assay and identifies distinct regulatory mechanisms in tumor versus normal cells.

Microenvironmental information significantly improves the recognition of cell types in human lung cancer patients

This study improves cell type classification in lung cancer tissue sections by incorporating microenvironment information through fisheye transformations, achieving human-level accuracy.

A Morpho-Proteomic Atlas of Mitosis at Sub-Minute Resolution

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.

Deep visual multi-omics profiling reveals mechanisms that underlie cancer cell differentiation and aggressiveness in clear cell renal cell carcinoma

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.

Regression plane concept: analysing continuous cellular processes with machine learning

The paper introduces Regression Plane, a tool for continuous phenotypic analysis using machine learning without requiring class labels.

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.