Michał Bryliński

This study investigates how genetic variants influence gene regulation in cancer by integrating GWAS data with 3D genome structure to identify functional mechanisms of non-coding SNPs.

GraphSite classifies ligand-binding sites using a deep graph neural network, achieving 81.28% test accuracy on 14 classes.

GraphGR predicts the effect of kinase inhibitors on cancer cell growth using a graph neural network that integrates heterogeneous data, achieving high accuracy on unseen data.

This study compares three algorithms for identifying similar ligand-binding pockets in proteins and finds that G-LoSA performs best, while combining algorithms improves performance.