Leonidas G. Alexopoulos

This study develops media formulations to mimic the physiological conditions of healthy and degenerate intervertebral discs and assesses their effects on cell viability and secretome.

This study reveals how ARID1A-mediated transcriptional rewiring drives resistance to MAPK inhibitors in melanoma by altering signalling pathways, immune interactions, and receptor dynamics, highlighting potential targets for combinatorial therapies.

The paper uses multilayer network analysis and multi-omics data to identify biological pathways in MS that explain disease severity and central nervous system damage.

This paper introduces deepSNEM, a graph deep learning model for compound mechanism of action identification using signaling network embeddings from gene expression data.

The paper predicts combination therapies for MS by modeling patient-specific signaling networks using phosphoproteomics data and identifies TAK1 as a co-druggable target.

This paper develops a computational method using WGCNA to identify gene modules in multi-tissue OA data and prioritizes compounds for drug discovery.

The paper presents a method to qualitatively model signaling networks by integrating experimental data and protein connectivity, showing that senescent cells are less responsive to insulin and activate senescence pathways.