Michael G. Ozawa

This study uses deep learning to predict YAP1/TEAD activity from histology slides, linking histological patterns to disease aggressiveness in cancer patients.

This paper introduces RNA-GAN, a deep generative model that creates synthetic whole-slide image tiles using gene expression profiles, outperforming traditional GANs and showing imputation capabilities.

The paper introduces REMI, a graph-based method to reconstruct cell-cell interactions in lung adenocarcinoma by accounting for ligand-receptor dependencies, and validates its findings experimentally.