Marija Pizurica

Towards Digital Quantification of Ploidy from Pan-Cancer Digital Pathology Slides using Deep Learning

PloiViT is a transformer-based model that quantifies tumor ploidy from pathology slides, outperforming traditional methods and achieving low prediction error across multiple cancer types.

GeNNius: An ultrafast drug-target interaction inference method based on graph neural networks

GeNNius uses a graph neural network to predict drug-target interactions more efficiently and accurately than existing methods across multiple datasets.

Digital profiling of cancer transcriptomes from histology images with grouped vision attention

This paper introduces SEQUOIA, a transformer model that predicts cancer transcriptomes from histology images, achieving high accuracy in predicting gene expression and recurrence risk in breast cancer.

Synthetic whole-slide image tile generation with gene expression profiles infused deep generative models

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.

RNA-to-image multi-cancer synthesis using cascaded diffusion models

This paper introduces RNA-Cascaded-Diffusion-Model to generate synthetic whole-slide image tiles from RNA-Seq data, improving machine learning model performance in low-data settings.